Cell-free detection of methylated tumour dna

ABSTRACT

Provided herein is a method for detecting a tumour that can be applied to cell-free samples, e.g., to cell-free detect circulating tumour DNA. The method utilizes detection of adjacent methylation signals within a single sequencing read as the basic positive tumour signal, thereby decreasing false positives. The method comprises extracting DNA from a cell-free sample obtained from a subject, bisulphite converting the DNA, amplifying regions methylated in cancer (e.g., CpG islands, CpG shores, and/or CpG shelves), generating sequencing reads, and detecting tumour signals. To increase sensitivity, biased primers designed based on bisulphite converted methylated sequences can be used. Target methylated regions can be selected from a pre-validated set according to the specific aim of the test. Absolute number, proportion, and/or distribution of tumour signals may be used for tumour detection or classification. The method is also useful in, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumour load, relapse, cancer development, or risk.

FIELD

This disclosure relates generally to tumour detection. More particularly, this disclosure relates to tumour-specific DNA methylation detection.

BACKGROUND

Cancer screening and monitoring has helped to improve outcomes over the past few decades simply because early detection leads to a better outcome as the cancer can be eliminated before it has spread. In the case of breast cancer, for instance, physical breast exams, mammography, ultrasound and MRI (in high risk patients) have all played a role in improving early diagnosis. The cost/benefit of these modalities for general screening, particularly in relatively younger women, has been controversial.

A primary issue for any screening tool is the compromise between false positive and false negative results (or specificity and sensitivity) which lead to unnecessary investigations in the former case, and ineffectiveness in the latter case. An ideal test is one that has a high Positive Predictive Value (PPV), minimizing unnecessary investigations but detecting the vast majority of cancers. Another key factor is what is called “detection sensitivity”, to distinguish it from test sensitivity, and that is the lower limits of detection in terms of the size of the tumour. Screening mammography in breast cancer, for instance, is considered to have a sensitivity from 80 to 90% with a specificity of 90%. However the mean size of tumours detected by mammography remains in the range of 15 to 19 mm. It has been suggested that only 3-13% of women derive an improved treatment outcome from this screening suggesting that the detection of smaller tumours would provide increased benefit. For women at high risk of developing breast cancer the use of MRI has offered some benefit with sensitivities in the range of 75 to 97% and specificities in the area of 90 to 96% and in combination with mammography offering 93-94% sensitivity and 77 to 96% specificities. However, MRI is acknowledged to have a poor PPV, in the area of 10-20%, leading to a large number of false positives and as a consequence unnecessary invasive investigations. All of these screens have likely reached their limit of detection sensitivity (or size of the tumour) and in the case of mammography still involve exposure to radiation, which may be of particular concern in women with familial mutations which render them more sensitive to radiation damage. There are no effective blood based screens for breast cancer based on circulating analytes.

While the above discussion focusses on breast cancer as an example, many of the same challenges exist for other types of cancers as well.

The detection of circulating tumour DNA is increasingly acknowledged as a viable “liquid biopsy” allowing for the detection and informative investigation of tumours in a non-invasive manner. Typically using the identification of tumour specific mutations these techniques have been applied to colon, breast and prostate cancers. Due to the high background of normal DNA present in the circulation these techniques can be limited in sensitivity. As well, the variable nature of tumour mutations in terms of occurrence and location (such as p53 and KRAS mutations) has generally limited these approaches to detecting tumour DNA at 1% of the total DNA in serum. Advanced techniques such as BEAMing have increased sensitivity, but are still limited overall. Even with these limitations the detection of circulating tumour DNA has recently been shown to be useful for detecting metastasis in breast cancer patients.

The detection of tumour specific methylation in the blood has been proposed to offer distinct advantages over the detection of mutations¹⁻⁵. A number of single or multiple methylation biomarkers have been assessed in cancers including lung⁶⁻¹⁰, colon^(11,12) and breast¹³⁻¹⁶. These have suffered from low sensitivities as they have tended to be insufficiently prevalent in the tumours. Several multi-gene assays have been developed with improved performance. A more advanced multi-gene system using a combination of 10 different genes has been reported and uses a multiplexed PCR based assay¹⁷. It offers combined sensitivity and specificity of 91% and 96% respectively, due to the better coverage offered and it has been validated in a small cohort of stage IV patients. However, it has a very high background in normal blood which will limit its detection sensitivity. Methylated markers have been used to monitor the response to neoadjuvant therapy^(18,19), and recently a methylation gene signature associated with metastatic tumours has been identified²⁰.

There remains a need for more sensitive and specific screening tools, as well as for straightforward tests that allow for the assessment of tumour burden, chemotherapy response, detection of residual disease, relapse and primary screening in high risk populations.

SUMMARY

It is an object of this disclosure to obviate or mitigate at least one disadvantage of previous approaches.

In a first aspect, this disclosure provides a method for detecting a tumour, comprising: extracting DNA from a cell-free sample obtained from a subject, bisulphite converting at least a portion of the DNA, amplifying regions methylated in cancer from the bisulphite converted DNA, generating sequencing reads from the amplified regions, and detecting tumour signals comprising at least two adjacent methylated sites within a single sequencing read, wherein the detection of at least one of the tumour signals is indicative of a tumour.

In another aspect, there is provided a use of the method for determining response to treatment.

In another aspect, there is provided a use of the method for monitoring tumour load.

In another aspect, there is provided a use of the method for detecting residual tumour post-surgery.

In another aspect, there is provided a use of the method for detecting relapse.

In another aspect, there is provided a use of the method as a secondary screen.

In another aspect, there is provided a use of the method as a primary screen.

In another aspect, there is provided a use of the method for monitoring cancer development.

In another aspect, there is provided a use of the method for monitoring cancer risk.

In another aspect, there is provided a kit for detecting a tumour comprising reagents for carrying out the method, and instructions for detecting the tumour signals.

Other aspects and features of this disclosure will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of this disclosure will now be described, by way of example only, with reference to the attached Figures.

FIG. 1 depicts a schematic of the method.

FIG. 2 depicts a schematic of the amplification of multiple target regions.

FIG. 3 lists 47 CpG targets selected to identify differentially methylated regions, and shows the results of Receiver Operator Curve (ROC) analysis.

FIG. 4 depicts histograms showing the frequency of patients binned according to positive (methylated) probe frequency. Panel A depicts results for luminal tumours. Panel B depicts results for basal tumours.

FIG. 5 depicts sequencing results to assess methylation status of a region near the CHST11 gene (CHST11 Probe C) in breast cancer cell lines.

FIG. 6 depicts sequencing results to assess methylation status of CHST11 Probe A in breast cancer tumors and normal breast tissue.

FIG. 7 depicts sequencing results to assess methylation status of FOXA Probe A in breast cancer cell lines.

FIG. 8 depicts sequencing results to assess methylation status of CHST Probe A and Probe B in prostate cancer cell lines.

FIG. 9 depicts sequencing results to assess methylation status of FOXA Probe A in prostate cancer cell lines.

FIG. 10 depicts sequencing results to assess methylation status of NT5 Probe E in breast cancer cell lines.

FIG. 11 depicts a summary of BioAnalyzer electrophoresis summary for amplification product generated from various cell lines.

FIGS. 12A and 12B depict a numerical summary of validation data generated for 98 different probes by bisulphite sequencing six different cell lines. # Reads is indicative of the number of reads exported, and Mean Me is indicative of the mean methylation.

FIGS. 13A and 13B depict a numerical summary of generated methylation data for tumour samples. # Reads is indicative of the number of reads exported, and Mean Me is indicative of the mean methylation.

FIG. 14 depicts a numerical summary generated methylation data for prostate cell lines. # Reads is indicative of the number of reads exported, and Mean Me is indicative of the mean methylation.

FIG. 15 is a diagram showing validation of various uveal melanoma (UM) probes in two cell lines MP38 (with loss of 3p) and MP41 (3p WT). Negative controls were cell free DNA (cfDNA) consisting of a pool of 18 individuals without cancer and peripheral mononuclear cells (PBMC). Probes for the indicated regions were PCR amplified individually and sequenced. Darker shading indicates higher level of methylation. OST3F was methylated in PBMCs while LDL3F was not methylated in tumours, with the majority showing strong methylation in the UM lines but not in the PBMCs or cfDNA.

FIG. 16 is a diagram showing methylation of cfDNA from patients with metastatic uveal melanoma. Methylated reads for each probe were extracted and all reads were normalized for the total number of reads in the sample. Stacked columns represent the total reads from all of the individual probes with different probes identified by shading. The patients are sorted by PAP measurements with high values on the left and lower values on the right. cfDNA is a pool of cell free DNA from 18 normal donors.

FIG. 17 is a diagram showing methylation of cfDNA from patients with metastatic uveal melanoma. Methylated reads for each probe were extracted and all reads were normalized for the total number of reads in the sample. Stacked columns represent the total reads from all of the individual probes with different probes identified by shading. The patients are sorted by tumour volume with larger volume on the left and lower volume on the right, and the volume indicated at the bottom. PAP values obtained from these patients is indicated. <5 refers to no detection of ctDNA in these samples. cfDNA is a pool of cell free DNA from 18 normal donors.

FIGS. 18A and 18B are diagrams showing methylation of cfDNA from sequential blood samples of two patients who were part of the patient groups shown in FIGS. 17 and 18. In FIG. 19A the patient was retested after seven months and the tumour at that time was assessed as being 0.5 cm³ in volume. In FIG. 19B the patient was retested after four months where the initial tumour volume was 483 cm³.

FIG. 19 is a log-log plot showing assay values (methylated reads) are correlated with tumour volume. The character of the metastatic tumour such as whether it is a solid mass or dispersed (miliary) was not taken into account.

FIG. 20 is a log-log plot showing relationship between test results and PAP signal, where PAP and methylation signals were correlated at higher PAP levels (trend line), although below the detection threshold of PAP at 5 copies/ml (vertical dashed line) the PAP signals were not correlated (ellipse).

FIG. 21 is a heat map of gene methylation in indicated prostate cancer cell lines.

FIG. 22 is a heat map of multiplexed probes for each prostate cancer patient sample. Patient samples were taken before the initiation of ADT (START) and 12 months after (M12). A black square indicates that methylated reads having greater than 80% methylation per read were detected for that probe but does not take into consideration the number of reads for each.

FIG. 23 is a diagram showing number of methylated reads per probe for each prostate cancer patient sample. Different probes are shown in different shading. The number of reads that were at least 80% methylated were determined for each sample and all probes are stacked per sample. Patient samples were taken before the initiation of ADT (START) and 12 months after (M12).

FIG. 24 is a plot showing normalized methylation reads per sample verses PSA levels for each patient. The totals of normalized methylated reads for all probes are plotted with solid lines. Patients initiated androgen deprivation therapy (START) and PSA levels measured at that time and after 12 months of treatment (M12) and are indicated with dashed lines. The methylation detection of circulating tumour DNA (mDETECT) test was performed on 0.5 ml of plasma from these same time points. The Gleason score for each patient at initial diagnosis is shown along with grading, as is the treatment applied as primary therapy (RRP, radical retropubic prostatectomy; BT, brachytherapy; EBR, external beam radiation; RT, radiotherapy).

FIG. 25 is a plot of TOGA prostate cancer tumour data, showing the average methylation for each of various Gleason groups, as well as for normal tissue from breast, prostate, lung, and colon, verses position on the genome (in this case on chromosome 8 for the region upstream of the TCF24 gene, a transcription factor of unknown function and PRSS3, a serine protease gene on chromosome 9).

FIGS. 26A, 26B, and 26C are charts showing regions used to develop a breast cancer test according to one embodiment. The chromosomal location and nucleotide position of the first CpG residue in the region is indicated. The TOGA breast cancer cohort was divided into sub-groups based on PAM-50 criteria. The fraction of each group that is positive for that probe is indicated. “Tissue” indicates results from normal tissue samples.

FIG. 27 shows theoretical area under the curve analyses of blood tests using the top 20 probes for each breast cancer subtype (LumA, LumB, Basal, HER2). These values were compared against normal tissue samples for the same probes.

FIG. 28 is a heatmap of multiplexed probes for each TNBC tumour sample and selected normal samples. A black square indicates that methylated reads having greater than 80% methylation per read were detected for that probe but does not take into consideration the number of reads for each.

FIG. 29 is a diagram showing results of a sensitivity test for TNBC to detect low levels of tumour DNA, using HCC1937 DNA diluted into a fixed amount of PBMC DNA (10 ng). Shaded squares indicate a distinct methylation signature.

FIG. 30 is a flowchart illustrating a method for determining biological methylation signatures, and for developing probes for their detection.

DETAILED DESCRIPTION

Generally, this disclosure provides a method for detecting a tumour that can be applied to cell-free samples, e.g., to detect cell-free circulating tumour DNA. The method utilizes detection of adjacent methylation signals within a single sequencing read as the basic “positive” tumour signal.

In one aspect, there is provided a method for detecting a tumour, comprising: extracting DNA from a cell-free sample obtained from a subject, bisulphite converting at least a portion of the DNA, amplifying regions methylated in cancer from the bisulphite converted DNA, generating sequencing reads from the amplified regions, and detecting tumour signals comprising at least two adjacent methylated sites within a single sequencing read, wherein the detection of at least one of the tumour signals is indicative of a tumour.

By “cell-free DNA (cfDNA)” is meant DNA in a biological sample that is not contained in a cell. cfDNA may circulate freely in in a bodily fluid, such as in the bloodstream.

“Cell-free sample”, as used herein, is meant a biological sample that is substantially devoid of intact cells. This may be a derived from a biological sample that is itself substantially devoid of cells, or may be derived from a sample from which cells have been removed. Example cell-free samples include those derived from blood, such as serum or plasma; urine; or samples derived from other sources, such as semen, sputum, feces, ductal exudate, lymph, or recovered lavage.

“Circulating tumour DNA”, as used herein, accordingly refers to cfDNA originating from a tumour.

By “region methylated in cancer” is meant a segment of the genome containing methylation sites (CpG dinucleotides), methylation of which is associated with a malignant cellular state. Methylation of a region may be associated with more than one different type of cancer, or with one type of cancer specifically. Within this, methylation of a region may be associated with more than one subtype, or with one subtype specifically.

The terms cancer “type” and “subtype” are used relatively herein, such that one “type” of cancer, such as breast cancer, may be “subtypes” based on e.g., stage, morphology, histology, gene expression, receptor profile, mutation profile, aggressiveness, prognosis, malignant characteristics, etc. Likewise, “type” and “subtype” may be applied at a finer level, e.g., to differentiate one histological “type” into “subtypes”, e.g., defined according to mutation profile or gene expression.

By “adjacent methylated sites” is meant two methylated sites that are, sequentially, next to each other. It will be understood that this term does not necessarily require the sites to actually be directly beside each other in the physical DNA structure. Rather, in a sequence of DNA including spaced apart methylation sites A, B, and C in the context A-(n)_(n)-B-(n)_(n)-C, wherein (n)_(n) refers to the number of base pairs (bp) (e.g., up to 300 bp), sites A and B would be recognized as “adjacent” as would sites B and C. Sites A and C, however, would not be considered to be adjacent methylated sites.

In one embodiment, the regions methylated in cancer comprise CpG islands.

“CpG islands” are regions of the genome having a high frequency of CpG sites. CpG islands are usually 300-3000bp in length and are found at or near promotors of approximately 40% of mammalian genes. They show a tendency to occur upstream of so-called “housekeeping genes”. A concrete definition is elusive, but CpG islands may be said to have an absolute GC content of at least 50%, and a CpG dinucleotide content of at least 60% of what would be statistically expected. Their occurrence at or upstream of the 5′ end of genes may reflect a role in the regulation of transcription, and methylation of CpG sites within the promoters of genes may lead to silencing. Silencing of tumour suppressors by methylation is, in turn, a hallmark of a number of human cancers.

In one embodiment, the regions methylated in cancer comprise CpG shores.

“CpG shores” are regions extending short distances from CpG islands in which methylation may also occur. CpG shores may be found in the region 0 to 2 kb upstream and downstream of a CpG island.

In one embodiment, the regions methylated in cancer comprise CpG shelves.

“CpG shelves” are regions extending short distances from CpG shores in which methylation may also occur. CpG shelves may generally be found in the region between 2 kb and 4 kb upstream and downstream of a CpG island (i.e., extending a further 2 kb out from a CpG shore).

In one embodiment, the regions methylated in cancer comprise CpG islands and CpG shores.

In one embodiment, the regions methylated in cancer comprise CpG islands, CpG shores, and CpG shelves.

In one embodiment, the regions methylated in cancer comprise CpG islands and sequences 0 to 4 kb upstream and downstream. The regions methylated in cancer may also comprise CpG islands and sequences 0 to 3 kb upstream and downstream, 0 to 2 kb upstream and downstream, 0 to 1 kb upstream and downstream, 0 to 500 bp upstream and downstream, 0 to 400 bp upstream and downstream, 0 to 300 bp upstream and downstream, 0 to 200 bp upstream and downstream, or 0 to 100 bp upstream and downstream.

In one embodiment, the step of amplifying is carried out with primers designed to anneal to bisulphite converted target sequences having at least one methylated site therein. Bisulphite conversion results in unmethylated cytosines being converted to uracil, while 5-methylcytosine is unaffected. “Bisulphite converted target sequences” are thus understood to be sequences in which cytosines known to be methylation sites are fixed as “C” (cytosine), while cytosines known to be unmethylated are fixed as “U” (uracil; which can be treated as “T” (thymine) for primer design purposes). Primers designed to target such sequences may exhibit a degree of bias towards converted methylated sequences. However, in one embodiment, the primers are designed without preference as to location of the at least one methylated site within target sequences. Often, to achieve optimal discrimination, it may be desirable to place a discriminatory base at the ultimate or penultimate 3′ position of an oligonucleotide PCR primer. In this embodiment, however, no preference is given to the location of the discriminatory sites of methylation, such that overall primer design is optimized based on sequence (not discrimination). This results in a degree of bias for some primer sets, but usually not complete specificity towards methylated sequences (some individual primer pairs, however, may be specific if a discriminatory site is fortuitously placed). As will be described herein, this permits some embodiments of the method to be quantitative or semi-quantitative.

In one embodiment, the PCR primers are designed to be methylation specific. This may allow for greater sensitivity in some applications. For instance, primers may be designed to include a discriminatory nucleotide (specific to a methylated sequence following bisulphite conversion) positioned to achieve optimal discrimination, e.g. in PCR applications. The discriminatory may be positioned at the 3′ ultimate or penultimate position.

In one embodiment, the primers are designed to amplify DNA fragments 75 to 150 bp in length. This is the general size range known for circulating DNA, and optimizing primer design to take into account target size may increase the sensitivity of the method according to this embodiment. The primers may be designed to amplify regions that are 50 to 200, 75 to 150, or 100 or 125 bp in length.

In some embodiments, concordant results provide additional confidence in a positive tumour signal. By “concordant” or “concordance”, as used herein, is meant methylation status that is consistent by location and/or by repeated observation. As has already been stated, the basic “tumour signal” defined herein comprises at least two adjacent methylated sites within a single sequencing read. However, additional layers of concordance can be used to increase confidence for tumour detection, in some embodiments, and not all of these need be derived from the same sequencing read. Layers of concordance that may provide confidence in tumor detection may include, for example:

(a) detection of methylation of at least two adjacent methylation sites;

(b) detection of methylation of more than two adjacent methylation sites;

(c) detection of methylation at adjacent sites within the same section of a target region amplified by one primer pair;

(d) detection of methylation at non-adjacent sites within the same section of a region amplified by one primer pair;

(e) detection of methylation at adjacent sites within the same target region;

(f) detection of methylation at non-adjacent sites within the same target region;

(g) any one of (a) to (f) in the same sequencing read;

(h) any one of (a) to (f) in at least two sequencing reads;

(i) any one of (a) to (f) in a plurality of sequencing reads;

(j) detection over methylation at sets of adjacent sites that overlap;

(k) repeated observation of any one of (a) to (j); or

(l) any combination or subset of the above.

In one embodiment, each of the regions is amplified in sections using multiple primer pairs. In one embodiment, these sections are non-overlapping. The sections may be immediately adjacent or spaced apart (e.g. spaced apart up to 10, 20, 30, 40, or 50 bp). Since target regions (including CpG islands, CpG shores, and/or CpG shelves) are usually longer than 75 to 150 bp, this embodiment permits the methylation status of sites across more (or all) of a given target region to be assessed.

A person of ordinary skill in the art would be well aware of how to design primers for target regions using available tools such as Primer3, Primer3Plus, Primer-BLAST, etc. As discussed, bisulphite conversion results in cytosine converting to uracil and 5′-methyl-cytosine converting to thymine. Thus, primer positioning or targeting may make use of bisulphite converted methylate sequences, depending on the degree of methylation specificity required.

Target regions for amplification are designed to have at least two CpG dinucleotide methylation sites. In some embodiments, however, it may be advantageous to amplify regions having more than one CpG methylation site. For instance, the amplified regions may have 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 CpG methylation sites. In one embodiment, the primers are designed to amplify DNA fragments comprising 3 to 12 CpG methylation sites. Overall this permits a larger number of adjacent methylation sites to be queried within a single sequencing read, and provides additional certainty (exclusion of false positives) because multiple concordant methylations can be detected within a single sequencing read. In one embodiment, the tumour signals comprise more than two adjacent methylation sites within the single sequencing read. Detecting more than two adjacent methylation sites provides additional concordance, and additional confidence that the tumour signal is not a false positive in this embodiment. For example, a tumour signal may be designated as 3, 4, 5, 6, 7, 8, 9, 10 or more adjacent detected methylation sites within a single sequencing read. In one embodiment, the detection of more than one of the tumour signals is indicative of a tumour. Detection of multiple tumour signals, in this embodiment, can increase confidence in tumour detection. Such signals can be at the same or at different sites. In one embodiment, the detection of more than one of the tumour signals at the same region is indicative of a tumour. Detection of multiple tumour signals indicative of methylation at the same site in the genome, in this embodiment, can increase confidence in tumour detection. So too can detection of methylation at adjacent sites in the genome, even if the signals are derived from different sequencing reads. This reflects another type of concordance. In one embodiment, the detection of adjacent or overlapping tumour signals across at least two different sequencing reads is indicative of a tumour. In one embodiment, the adjacent or overlapping tumour signals are within the same CpG island. In one embodiment, the detection of 5 to 25 adjacent methylated sites is indicative of a tumour.

Methylated regions can be selected according to the purpose of the intended assay. In one embodiment, the regions comprise at least one region listed Table 1 and/or Table 2. In one embodiment, the regions comprise all regions listed in Table 1 and/or Table 2.

Likewise, primer pairs can be designed based on the intended target regions.

In one embodiment, the step of amplification is carried out with more than 100 primer pairs. The step of amplification may be carried out with 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, or more primer pairs. In one embodiment, the step of amplification is a multiplex amplification. Multiplex amplification permits large amount of methylation information to be gathered from many target regions in the genome in parallel, even from cfDNA samples in which DNA is generally not plentiful. The multiplexing may be scaled up to a platform such as ION AmpliSeq™, in which, e.g. up to 24,000 amplicons may be queried simultaneously. In one embodiment, the step of amplification is nested amplification. A nested amplification may improve sensitivity and specificity.

The nested reaction may be part of a next generation sequencing approach. Barcode and/or sequencing primers may be added in the second (nested) amplification. Alternatively, these may added in the first amplification.

In one embodiment, the method further comprises quantifying the tumour signals, wherein a number in excess of a threshold is indicative of a tumour. In one embodiment, the steps of quantifying and comparing are carried out independently for each of the sites methylated in cancer. Accordingly, a count of positive tumour signals may be established for each site. In one embodiment, the method further comprises determining a proportion of the sequencing reads containing tumour signals, wherein the proportion in excess of a threshold is indicative of a tumour. In one embodiment, the step of determining is carried out independently for each of the sites methylated in cancer.

By “threshold”, as used herein, is meant a value that is selected to discriminate between a disease (e.g., malignant) state, and a non-disease (e.g., healthy) state. Thresholds can be set according to the disease in question, and may be based on earlier analysis, e.g., of a training set. Thresholds may also be set for a site according to the predictive value of methylation at a particular site. Thresholds may be different for each methylation site, and data from multiple sites can be combined in the end analysis.

Various design parameters may be used to select the regions subject to amplification in some embodiments. In one embodiment, the regions are not methylated in healthy tissue. Healthy tissue would be understood to be non-malignant. Healthy tissue is often selected based on the origin of the corresponding tumour.

Regions may be selected based on desired aims or required specificity, in some embodiments. For instance, it may be desirable to screen for more than one cancer type. Thus, in one embodiment, the regions are collectively methylated in more than one tumour type. It may be desirable to include regions methylated generally in a group of cancers, and regions methylated in specific cancers in order to provide different tiers of information. Thus, in one embodiment, the regions comprise regions that are specifically methylated in specific tumours, and regions that are methylated in more than one tumour type. Likewise, it may be desirably to include a second tier of regions that can differentiate between tumour types. In one embodiment, the regions specifically methylated in specific tumours comprise a plurality of groups, each specific to one tumour type. However, it may be desirable in some contexts to have a test that is focused on one type of cancer. Thus, in one embodiment, the regions are methylated specifically in one tumour type. In one embodiment, the regions are selected from those listed in Table 3 and the tumour is one carrying a BRCA1 mutation.

More specifically, in some embodiments regions may be selected that are methylated in particular subtypes of a cancer exhibiting particular histology, karyotype, gene expression (or profile thereof), gene mutation (or profile thereof), staging, etc. Accordingly, the regions to be amplified may comprise one or more groups of regions, each being established to be methylated in one particular cancer subtype. In one embodiment the regions to be amplified may be methylated in a cancer subtype bearing particular mutations. With breast cancer in mind, one example subtype defined by mutation is cancer bearing BRCA1 mutations. Another subtype is cancer bearing BRCA2 mutations. Other breast cancer subtypes for which methylated regions may be determined include Basal, Luminal A, Luminal B, HER2 and Normal-like tumours. For uveal melanoma, for example, subtypes may include tumours that have retained or lost chromosome 3 (monosomy 3).

Within the context of such a test of some embodiments, information about not only the presence, but also the pattern and distribution of tumour signals both within specific regions and between different regions may help to detect or validate the presence of a form of cancer. In one embodiment, the method further comprises determining a distribution of tumour signals across the regions, and comparing the distribution to at least one pattern associated with a cancer, wherein similarity between the distribution and the pattern is indicative of the cancer.

“Distribution”, as used herein in this context, is meant to indicate the number and location of tumour signals across the regions. Statistical analysis may be used to compare the observed distribution with, e.g., pre-established patterns (data) associated with a form of cancer. In other embodiments, the distribution may be compared to multiple patterns. In one embodiment, the method further comprises determining a distribution of tumour signals across the regions, and comparing the distribution to a plurality of patterns, each one associated with a cancer type, wherein similarity between the distribution and one of the plurality of patterns is indicative of the associated cancer type.

In one embodiment, the step of generating sequencing reads is carried out by next generation sequencing. This permits a very high depth of reads to be achieved for a given region. These are high-throughput methods that include, for example, Ilumina (Solexa) sequencing, Roche 454 sequencing, Ion Torrent sequencing, and SOLiD sequencing. The depth of sequencing reads may be adjusted depending on desired sensitivity.

In one embodiment, the step of generating sequencing reads is carried out simultaneously for samples obtained from multiple patients, wherein the amplified CpG islands from is barcoded for each patient. This permits parallel analysis of a plurality of patients in one sequencing run.

A number of design parameters may be considered in the selection of regions methylated in cancer, according to some embodiments. Data for this selection process may be from a variety of sources such as, e.g., The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/), derived by the use of, e.g., Illumina Infinium HumanMethylation450 BeadChip (http://www.illumina.com/products/methylation450beadchipkits.html) for a wide range of cancers, or from other sources based on, e.g., bisulphite whole genome sequencing, or other methodologies. For instance, “methylation value” (understood herein as derived from TCGA level 3 methylation data, which is in turn derived from the beta-value, which ranges from −0.5 to 0.5) may be used to select regions. In one embodiment, the step of amplification is carried out with primer sets designed to amplify at least one methylation site having a methylation value of below -0.3 in normal issue. This can be established in a plurality of normal tissue samples, for example 4. The methylation value may be at or below −0.1, −0.2, −0.3, −0.4, or −0.5. In one embodiment, the primer sets are designed to amplify at least one methylation site having a difference between the average methylation value in the cancer and the normal tissue of greater than 0.3. The difference may be greater than 0.1, 0.2, 0.3, 0.4, or 0.5. Proximity of other methylation sites that meet this requirement may also play a role in selecting regions, in some embodiments. In one embodiment, the primer sets include primer pairs amplifying at least one methylation site having at least one methylation site within 200 bp that also has a methylation value of below −0.3 in normal issue, and a difference between the average methylation value in the cancer and the normal tissue of greater than 0.3. In another embodiment the adjacent site having these features may be 300 bp. The adjacent site may be within 100, 200, 300, 400, or 500 bp.

In some embodiments, target regions may be selected for amplification based on the number of tumours in the validation set having methylation at that site. For example, a region may be selected if it is methylated in at least 50%, 55%, 60%, 65%, 70%, 75%, 80, 85%, 90, or 95% of tumours tested. For example, regions may be selected if they are methylated in at least 75% of tumours tested, including within specific subtypes. For some validations, it will be appreciated that tumour-derived cell lines may be used for the testing.

In another embodiment, the method further comprises oxidative bisulphite conversion. In addition to the analysis of methylation of CpG residues, additional information that may be of clinical significance may be derived from the analysis of hydroxymethylation. Bisulphite sequencing results in the conversion of unmethylated cytosine residues into uracil/thymidine residues, while both methylated and hydroxymethylated cytosines remain unconverted. However, oxidative bisulphite treatment allows for the conversion of hydroxymethylated cytosines to uracil/thymidine allowing for the differential analysis of both types of modifications. By comparison of bisulphite to oxidative bisulphite treatments the presence of hydroxymethylation can be deduced. This information may be of significance as its presence or absence may be correlated with clinical features of the tumor which may be clinically useful either as a predictive or prognostic factor. Accordingly, in some embodiments, information about hydroxymethylation could additionally be used in the above-described embodiments.

In one aspect, the presence of specific patterns of methylation is linked to underlying characteristics of particular tumours. In these cases, the methylation patterns detected by the method are indicative of clinically relevant aspects of the tumours such as aggressiveness, likelihood of recurrence, and response to various therapies. Detection of these patterns in the blood may thus provide both prognostic and predictive information related to a patient's tumor.

In another aspect, the forgoing method may be applied to clinical applications involving the detection or monitoring of cancer.

In one embodiment, the forgoing method may be applied to determine and/or predict response to treatment.

In one embodiment, the forgoing method may be applied to monitor and/or predict tumour load.

In one embodiment, the forgoing method may be applied to detect and /or predict residual tumour post-surgery.

In one embodiment, the forgoing method may be applied to detect and/or predict relapse.

In one aspect, the forgoing method may be applied as a secondary screen.

In one aspect, the forgoing method may be applied as a primary screen.

In one aspect, the forgoing method may be applied to monitor cancer development.

In one aspect, the forgoing method may be applied to monitor and/or predict cancer risk.

In another aspect, there is provided a kit for detecting a tumour comprising reagents for carrying out the aforementioned method, and instructions for detecting the tumour signals. Reagents may include, for example, primer sets, PCR reaction components, and/or sequencing reagents.

In one embodiment of the forgoing methods, the regions comprise C2CD4A, COL19A1, DCDC2, DHRS3, GALNT3, HES5, KILLIN, MUC21, NR2E1/OSTM1, PAMR1, SCRN1, and SEZ6, and the tumour is uveal melanoma. In one embodiment, the probes comprise C2C5F, COL2F, DCD5F, DGR2F, GAL1F, GAL3F, HES1F, HES3F, HES4F, KIL5F, KIL6F, MUC2F, OST3F, OST4F, PAM4F, SCR2F, SEZ3F, and SEZ5F.

In one embodiment, the regions comprise ADCY4, ALDH1L1, ALOX5, AMOTL2, ANXA2, CHST11, EFS, EPSTI1, EYA4, HAAO, HAPLN3, HCG4P6, HES5, HIF3A, HLA-F, HLA-J, HOXA7, HSF4, KLK4, LOC376693, LRRC4, NBR1, PAH, PON3, PPM1H, PTRF, RARA, RARB, RHCG, RND2,TMP4, TXNRD1, and ZSCAN12, and the tumour is prostate cancer. In one embodiment, the probes comprise ADCY4-F, ALDH1L1-F, ALOX5-F, AMOTL2-F, ANXA2-F, CHST11-F, EFS-F, EPSTI1-F, EYA4-F, HAAO-F, HAPLN3-F, HCG4P6-F, HES5-F, HIF3A-F, HLA-F-F, HLA-J-1-F, HLA-J-2-F, HOXA7-F, HSF4-F, KLK4-F, LOC376693-F, LRRC4-F, NBR1-F, PAH-F, PON3-F, PPM1H-F, PTRF-F, RARA-F, RARB-F, RHCG-F, RND2-F,TMP4-F, TXNRD1-F, and ZSCAN12-F. In one embodiment, the probes additionally include C1Dtrim, C1Etrim, CHSAtrim, DMBCtrim, FOXAtrim, FOXEtrim, SFRAtrim, SFRCtrim, SFREtrim, TTBAtrim, VWCJtrim, and VWCKtrim.

In one embodiment, the regions comprise ASAP1, BC030768, C18orf62, C6orf141, CADPS2, CORO1C, CYP27A1, CYTH4, DMRTA2, EMX1, HFE, HIST1H3G/1H2BI, HMGCLL1, KCNK4, KJ904227, KRT78, LINC240, Me3, MIR1292, NBPF1, NHLH2, NRN1, PPM1H, PPP2R5C, PRSS3, SFRP2, SLCO4C1, SOX2OT, TUBB2B, USP44, Intergenic (Chr1), Intergenic (Chr2), Intergenic (Chr3), Intergenic (Chr4), Intergenic (Chr8), and Intergenic (Chr10), and the tumour is aggressive prostate cancer. In one embodiment, the aggressive prostate cancer has a Gleason Score greater than 6. In one embodiment, the aggressive prostate cancer has a Gleason Score of 9 or greater. In one embodiment, the probes comprise ASAP1/p, BC030768/p, C18orf62/p, C6orf141/p-1, C6orf141/p-2, CADPS2/p, CORO1C/p-1, CORO1C/p-2, CYP27A1/p, CYTH4/p, DMRTA2/p, EMX1/p, HFE/p-1, HFE/p-2, HIST1H3G/1H2BI/p, HMGCLL1/p, KCNK4/p, KJ904227/p, KRT78/p, LINC240/p-1, LINC240/p-2, Me3/p-1, Me3/p-2, MIR129, NBPF1/p, NHLH2/p, NRN1/p, PPM1H/p-1, PPM1H/p-2, PPP2R5C/p, PRSS3/p, SFRP2/p-1, SFRP2/p-2, SLCO4C1/p, SOX2OT/p, TUBB2B/p, USP44/p, Chr1/p-1, Chr2/p-1, Chr3/p-1, Chr4/p-1, Chr8/p-1, and Chr10/p-1.

In one embodiment, the regions comprise the regions depicted in FIGS. 26A, 26B, and 26C, and the tumour is breast cancer.

In one embodiment, the regions comprise ALX1, ACVRL1, BRCA1,C1orf114, CA9, CARD11, CCL28, CD38, CDKL2, CHST11, CRYM, DMBX1, DPP10, DRD4, ERNA4, EPSTI1, EVX1, FABP5, FOXA3, GALR3, GIPC2, HINF1B, HOXA9, HOXB13, Intergenic5, Intergenic 8, IRF8, ITPRIPL1, LEF1,LOC641518, MAST1, BARHL2, BOLL, C5orf39, DDAH2, DMRTA2, GABRA4, ID4, IRF4, NTSE, SIM1, TBX15, NFIC, NPHS2, NR5A2, OTX2, PAX6, GNG4, SCAND3, TAL1, PDX1, PHOX2B, POU4F1,PFIA3, PRDM13, PRKCB, PRSS27, PTGDR, PTPRN2, SALL3, SLC7A4, SOX2OT, SPAG6, TCTEX1D1, TMEM132C, TMEM90B, TNFRSF10D, TOP2P1, TSPAN33, TTBK1, UDB, and VWC2., and the tumour is triple negative breast cancer (TNBC). In one embodiment, the probes comprise ALX1, AVCRL1, BRCA1-A, C1Dtrim, C1Etrim, CA9-A, CARD11-B, CCL28-A, CD38, CDKL2-A, CHSAtrim, CRYM-A, DMBCtrim, DMRTA2exp-A, DPP10-A, DPP10-B, DPP10-C, DRD4-A, EFNA4-B, EPSTI1, EVX1, FABP5, FOXAtrim, FOXEtrim, GALR3-A, GIPC2-A, HINF C trim, HOXAAtrim, HOXACtrim, HOXB13-A, Int5, Int8, IRF8-A, ITRIPL1, LEF1-A, MAST1 A trim, mbBARHL2 Trim, mbBOLL Trim, mbC5orf Trim, mbDDAH Trim, mbDMRTA Trim, mbGABRA A Trim, mbGABRA B Trim, mbGNG Trim, mbID4 Trim, mbIRF Trim, mbNT5E Trim, mbSIM A Trim, mbTBX15 Trim, NFIC-B, NFIC-A, NPSH2-B, NR5A2-B, OTX2-A, PAX6-A, pbDMRTA Trim, pbGNG Trim, pbSCAND Trim, pbTAL Trim, PDX1exp-B, PHOX2B-A, POU4F1 A trim, PPFIA3-A, PRDM13, PRKCB-A, PRKCB-C, PRSS27-A, PTGDR, PTPRN2-A, PTPRN2-B, SALL3-A, SALL3-B, SLC7A4-A, SOX2OT-B, SPAG6 A trim, TCTEX1D1-A, TMEM-A, TMEM-B, TMEM90B-A, TNFRSF10D, TOP2P1-B, TSPAN33-A, TTBAtrim, UBD-A, VWCJtrim, and VWCKtrim.

In one embodiment, each region is amplified with primer pairs listed for the respective region in Table 15.

In one embodiment, the method further comprises administering a treatment for the tumour detected.

In one aspect, there is provided a method for identifying a methylation signature indicative of a biological characteristic, the method comprising: obtaining data for a population comprising a plurality of genomic methylation data sets, each of said genomic methylation data sets associated with biological information for a corresponding sample, segregating the methylation data sets into a first group corresponding to one tissue or cell type possessing the biological characteristic and a second group corresponding to a plurality of tissue or cell types not possessing the biological characteristic, matching methylation data from the first group to methylation data from the second group on a site-by-site basis across the genome, identifying a set of CpG sites that meet a predetermined threshold for establishing differential methylation between the first and second groups, identifying, using the set of CpG sites, target genomic regions comprising at least two differentially methylated CpGs with 300 bp that meet said predetermined criteria, extending the target genomic regions to encompass at least one adjacent differentially methylated CpG site that does not meet the predetermined criteria, wherein the extended target genomic regions provide the methylation signature indicative of the biological trait.

In one embodiment, the method further comprises validating the extended target genomic regions by testing for differential methylation within the extended target genomic regions using DNA from at least one independent sample possessing the biological trait and DNA from at least one independent sample not possessing the biological sample.

In one embodiment, the step of identifying further comprises limiting the set of CpG sites to CpG sites that further exhibit differential methylation with peripheral blood mononuclear cells from a control sample.

In one embodiment, the plurality of tissue or cell types of the second group comprises at least some tissue or cells of the same type as the first group.

In one embodiment, the plurality of tissue or cell types of the second group comprises a plurality of non-diseased tissue or cell types.

In one embodiment, the predetermined threshold is indicative of methylation in the first group and non-methylation in the second group.

In one embodiment, the predetermined threshold is at least 50% methylation in the first group.

In one embodiment, the predetermined threshold is a difference in average methylation between the first and second groups of 0.3 or greater.

In one embodiment, the biological trait comprises malignancy.

In one embodiment, the biological trait comprises a cancer type.

In one embodiment, the biological trait comprises a cancer classification.

In one embodiment, the cancer classification comprises a cancer grade.

In one embodiment, the cancer classification comprises a histological classification.

In one embodiment, the biological trait comprises a metabolic profile.

In one embodiment, the biological trait comprises a mutation.

In one embodiment, the mutation is a disease-associated mutation.

In one embodiment, the biological trait comprises a clinical outcome.

In one embodiment, the biological trait comprises a drug response.

In one embodiment, the method further comprises designing a plurality of PCR primers pairs to amplify portions of the extended target genomic regions, each of the portions comprising at least one differentially methylated CpG site.

In one embodiment, the step of designing the plurality of primer pairs comprising converting non-methylated cytosines uracil, to simulate bisulphite conversion, and designing the primer pairs using the converted sequence.

In one embodiment, the primer pairs are designed to have a methylation bias.

In one embodiment, the primer pairs are methylation-specific.

In one embodiment, the primer pairs have no CpG residues within them having no preference for methylation status.

In one aspect, there is provided a method for synthesizing primer pairs specific to a methylation signature, the method comprising: carrying out the forgoing method, and synthesizing the designed primer pairs.

In one aspect, there is provided a non-transitory computer-readable medium comprising instructions that direct a processor to carry out the forgoing method.

In one aspect, there is provided a computing device comprising the computer-readable medium.

EXAMPLE 1

Concept Summary

The embodiments detect circulating tumour DNA using a highly sensitive and specific methylation based assay with detection limits 100 times better than other techniques.

FIG. 1 depicts a schematic of the overall strategy. CpG dinucleotides are often clustered into concentrated regions in the genome referred to as CpG islands (grey box) and are often, but not always, associated with the promoter or enhancer regions of genes. These regions are known to become abnormally methylated in tumours (CmpG) as compared to normal tissue (CpG) which may be linked to the inactivation of tumour suppressor genes by this methylation event. Methylation of CpG islands and the boundary regions (CpG island shores) is extensive and co-ordinated such that most or all of the CpG residues in that region become methylated. The detection of this methylation typically involves bisulphite conversion, PCR amplification of the relevant region (arrows), and sequencing where un-methylated CpG residues are converted to TpG dinucleotides while methylated CpG residues are preserved as CpGs. Sequencing of these PCR-amplified “probes” (BISULFITE SEQUENCING) from tumour DNA (arrows) results in the detection of multiple CpG residues being methylated within the same DNA fragment (Dashed Box) which can easily be distinguished from DNA from normal tissue (Boxes). The co-ordinated/concordant nature of this methylation produces a strong signal which can be detected over random or background changes from DNA sequencing. This is accomplished by first identifying regions of tumour specific DNA methylation with multiple correlated CpG methylation sites within the same region.

FIG. 30 depicts a flowchart showing how a methylation signature for a biological trait may be determined. One or more steps of this method may be implemented on a computer. Accordingly, another aspect of this disclosure relates to a non-transitory computer-readable medium comprising instructions that direct a processor to carry out steps of this method.

Generally “probe” is used herein to refer to a target region for amplification and/or the ensuing amplified PCR product. It will be understood that each probe is amplified by a “primer set” or “primer pair”.

FIG. 2 depicts a schematic for amplification of target regions. Multiple regions from across the human genome have been identified as being differentially methylated in the DNA from various types of tumours compared to the normal DNA from a variety of different tissues. These regions can be fairly extensive spanning 100s to 1000s of base pairs of DNA. These target regions (black boxes, bottom) exhibit coordinated methylation where most or all of the CpG dinucleotides in these regions are methylated in tumour tissue with little or no methylation in normal tissues. As shown in FIG. 2, when sequencing across these regions (arrows) multiple CpG residues are seen to be methylated together in the tumour creating a concordant signal identifiable as being tumour specific. By targeting multiple PCR-amplified probes across individual regions (middle) and across the entire genome (top) large numbers of probes can be designed with the advantage that with more probes comes greater sensitivity due to the greater likelihood of detecting a tumour specific fragment in a given sample. Primers for these probes are designed to amplify regions from 75 to 150 bp in length, corresponding to the typical size of circulating tumour DNA. The primers may include CpG dinucleotides or not, which in the former case can make these primers biased towards the amplification of methylated DNA or exclusively amplify only methylated DNA.

Multiple methylation-biased PCR primer pairs can be created, which are able to preferentially amplify these regions. These multiple regions are sequenced using next generation sequencing (NGS) at a high read depth to detect multiple tumour specific methylation patterns in a single sample. As described herein, features have been incorporated into a blood based cancer detection system that provides advantages over other tests which have been developed, and provides an unprecedented level of sensitivity and specificity as well as enables the detection of minute quantities of DNA (detection sensitivity).

EXAMPLE 2

Probe and Primer Set Development

The detection of circulating tumour DNA is hampered by both the presence of large amounts of normal DNA as well as by the very low concentrations of tumour DNA in the blood. Compounding this issue, both PCR and sequencing based approaches suffer from the introduction of single nucleotide changes due to the error prone nature of these processes. To deal with these issues, regions of the genome have been identified that exhibit concerted tumour specific methylation over a significant expanse of DNA so that each CpG residue is concordant²¹. Methylation-biased PCR primer pairs were designed for multiple segments of DNA across these regions each containing multiple CpG residues. Sample protocols for selection of differentially methylated regions and design of region specific PCR primers are provided.

Protocol For the Selection of Differentially Methylated Regions

Use of TCGA DATA For Identifying Breast Specific Probes

Level 3 (processed) Illumina Infinium HumanMethylation450 BeadChip array data (http://www.illumina.com/techniques/microarrays/methylation-arrays.html) was downloaded from The Tumour Genome Atlas (TCGA) site (https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp) for the appropriate tumour types (e.g., breast, prostate, colon, lung, etc.). Tumour and normal samples were separated and the methylation values (from −0.5 to +0.5) for each group were averaged. The individual methylation probes were mapped to their respective genomic location. Probes that fulfilled the following example criteria were then identified:

1. The average methylation values for the normal breast, prostate, colon and lung tissues all below −0.3;

2. The difference between the average breast tumour and average breast normal values greater than 0.3, or at least 50% methylation in the tumour group; and

3. Two probes within 300 bp of each other fulfill criteria 1 and 2.

These criteria establish that the particular probe is not methylated in normal tissue, that the difference between the tumour and normal is significant, and that multiple probes in a relatively small area are co-ordinately methylated. Regions which had multiple positive consecutive probes (i.e., 3 or more) were prioritized for further analysis. Average values for approximately 10 other probes to either side of the positive region were plotted for all tumour and normal tissue samples to define the region exhibiting differential methylation. Regions exhibiting concerted differential methylation between tumour and normal for single or multiple tumour types were identified.

A secondary screen for a lack of methylation of these regions in blood was carried out by examining the methylation status of the defined regions in multiple tissues using nucleotide level genome wide bisulphite sequencing data. Specifically the UCSC Genome Browser (https://genome.ucsc.edu/) was used to examine methylation data from multiple sources.

Data was processed by the method described in Song Q, et al., A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLOS ONE 2013 8(12): e81148 (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081148) for use in the UCSC Browser and to identify hypo-methylated regions (above blue lines).

The following data sources were used:

Gertz J, et al., Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet. 2011 7(8):e1002228 (http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002228).

Heyn H, et al., Distinct DNA methylomes of newborns and centenarians. Proc. Natl. Acad. Sci. U.S.A. 2012 109(26):10522-7 (http://www.pnas.org/content/109/26/10522).

Hon G C, et al., Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012 22(2):246-58 (http://genome.cshlp.org/content/22/2/246).

Heyn H, et al., Whole-genome bisulfite DNA sequencing of a DNMT3B mutant patient. Epigenetics. 2012 7(6):542-50 (http://www.tandfonline.com/doi/abs/10.4161/epi.20523#.VsS_gdIUVIw).

Hon G C, et al., Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012 22(2):246-58 (http://genome.cshlp.org/content/22/2/246).

All of the regions identified exhibited hypo-methylation in normal blood cells including Peripheral Blood Mononuclear Cells (PBMC), the prime source of non-tissue DNA in plasma.

Protocol For the Design of Region Specific Primers For PCR Amplification and Next Generation Sequencing

For regions identified as being differentially methylated in tumours, PCR primers were designed that are able to recognize bisulphite converted DNA which is methylated. Using Methyprimer Express™ or PyroMark™, or other web based programs, the DNA sequence of the region was converted to the sequence obtained when fully methylated DNA is bisulphite converted (i.e., C residues in a CpG dinucleotide remain Cs, while all other C residues are converted to T residues). The converted DNA was then analysed using PrimerBlast™ (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) to generate optimal primers. Primers were not expressly selected to contain CpG residues but due to the nature of the regions, generally CpG islands, most had 1 to 3 CpGs within them. This renders them biased towards the amplification of methylated DNA but in many cases they do recognize and amplify non-methylated DNA as well. The region between the primers includes 2 or more CpG residues. Primers were chosen to amplify regions from 75 to 150 base pairs in size with melting temperatures in the range of 52-68° C. Multiple primers were designed for each region to provide increased sensitivity by providing multiple opportunities to detect that region. Adapter sequences (CS1 and CS2) were included at the 5′ end of the primers to allow for barcoding and for sequencing on multiple sequencing platforms by the use of adaptor primers for secondary PCR.

Primers were characterized by PCR amplification of breast cancer cell line DNA and DNA from various primary tumours. PCR amplification was done with individual sets of primers and Next Generation Sequencing carried out to characterize the methylation status of specific regions. Primer sets exhibiting appropriate tumour specific methylation were then combined into a multiplex PCR reaction containing many primers.

Results

FIG. 3 lists the 47 CpG probes used to identify differentially methylated regions. These were analyzed by Receiver Operator Curve analysis (ROC). Normal and tumour samples from the entire TOGA breast cancer database were compared. The Area Under the Curve (AUC) analysis for each probe is shown with the standard error, 95% confidence interval and P-value. All of them where shown to have excellent discriminatory capabilities.

FIG. 4 depicts the results of analysis methylation level for each patient in the TOGA database for the 47 CpG. Those exceeding the threshold of −0.1 were considered to be positive for methylation in that patient. The number of probes exceeding this methylation threshold were calculated for each patient. Patients were divided into those with Luminal A and B subtypes (Luminal Tumours; FIG. 4, Panel A) and those with Basal cancers (Basal Tumours; FIG. 4, Panel B) or and the number of patients with a specific range of positive probes was calculated. The histogram shows the frequency of patents within each range of positive probes. While these probes give excellent coverage in both populations, there are more positive probes amongst the Luminal tumours than the Basal tumours. Additional probes specific to the different breast cancer subtypes have been identified and appropriate probe development and validation is underway.

EXAMPLE 3

Selection of Regions For Cancer and Cancer Types

For breast cancer, 52 regions in the genome were identified that are highly methylated in tumours but where multiple normal tissues do not exhibit methylation of these regions. These serve as highly specific markers for the presence of a tumour with little or no background signal.

Table 1 depicts regions selected for breast cancer screening.

TABLE 1 Chromo- Start End General some (hg18) (hg18) Location Tumour Size 2nd Generation chr1 167663259 167663533 C1orf114 P/B 274 chr7 49783577 49784309 VWC2 P/B/C 732 chr14 23873519 23873993 ADCY4 P/B/C 474 chr11 43559012 43559541 MIR129-2 B/C 529 3rd Generation chr6 43319186 43319213 TTBK1 P/B 27 chr1 46723905 46724176 DMBX1 P/B/C 271 chr7 27171684 27172029 HOXA9 B 345 chr8 120720175 120720579 ENPP2 P/B 404 chr10 99521635 99521924 SFRP5 P/B 289 chr12 103376281 103376485 CHST11 P/B/C 204 chr19 51071603 51072234 FOXA3 P/B 631 4th Generation chr1 47470535 47470713 TAL1 B 178 chr1 50658998 50659557 DMRTA2 B 559 chr1 66030610 66030634 PDE4B B 24 chr1 90967262 90967924 BARHL2 B 662 chr1 119331667 119332616 TBX15 B/C 949 chr1 153557070 153557585 RUSC1, B 515 C1orf104 chr1 233880632 233880962 GNG4 B 330 chr2 104836482 104837226 POU3F3 B 744 chr2 198359230 198359743 BOLL B/C 513 chr3 32834103 32834562 TRIM71 B/C 459 chr3 172228723 172228985 SLC2A2 B 262 chr4 5071985 5072137 CYTL1 B 152 chr4 42094549 42094615 SHISA3 B 66 chr4 46690266 46690578 GABRA4 B 312 chr5 38293273 38293312 EGFLAM B 39 chr5 43076195 43076642 C5orf39 B 447 chr5 115179918 115180393 CDO1 B 475 chr6 336189 337131 IRF4 B/C 942 chr6 19944994 19945298 ID4 B 304 chr6 28618285 28618318 SCAND3 B 33 chr6 31806197 31806205 DDAH2 B 8 chr6 33269254 33269355 COL11A2 B 101 chr6 86215822 86215929 NT5E B 107 chr6 101018889 101019751 SIM1 B 862 5th Generation chr6 153493505 153494425 RGS17 B 920 chr7 121743738 121744126 CAPDS2 B 388 chr8 72918338 72918895 MSC B/C 557 chr10 22674438 22674584 SPAG6 B/C 146 chr10 105026601 105026737 INA B 136 chr11 128068895 128069316 FLI1 B/C 421 chr12 52357158 52357378 ATP5G2 B 220 chr12 94466892 94467095 USP44 B/C 203 chr13 78075521 78075764 POU4F1 B 243 chr14 55656275 55656325 PELI2 B 50 chr17 33176853 33178091 HNF1B B 1238 chr17 32368343 32368604 LHX1 B/C/L 261 chr17 44154844 44155027 PRAC, B/C 183 C17orf93 chr18 73090725 73091121 GALR1 B/C 396 chr19 12839383 12839805 MAST1 B 422 chr20 2729122 2729438 CPXM1 B/C 316 chr20 43952209 43952500 CTSA, B 291 NEURL2

In Table 1, ‘Start’ and ‘End’ designate the coordinates of the target regions in the hg18 build of the human genome reference sequence. The ‘General Location’ field gives the name of one or more gene or ORF in the vicinity of the target region. Examination of these sequences relative to nearby genes indicates that they were found, e.g., in upstream, in 5′ promoters, in 5′ enhancers, in introns, in exons, in distal promoters, in coding regions, or in intergenic regions. The ‘Tumour’ field indicates whether a region is methylated in prostate (P), breast (B), colon (C), and/or lung (L) cancers. The ‘Size’ field indicates the size of the target region.

In the discussion here, it should be recognized that reference to genes such as CHST11, FOXA, and NT5 are not intended to be indicative of the genes in question per se, but rather to the associated methylated regions described in Table 1.

In total, 52 regions were found to be methylated in association with breast cancer, 17 were found to be methylated in association with prostate cancer, 9 were found to be methylated in association with prostate cancer, and 1 region was found to be methylated in association with lung cancer. Thus, some regions appear to be generally indicative of the various types of cancers assessed. Other regions methylated in subgroups of these, while others are specific for cancers. In the context of this assay and the types of cancers examined, 25 regions may be described as being “specifically methylated in breast cancer”. However, it is noted that the same approach may be used to identify regions methylated specifically in other cancers.

Assays may be developed for cancer generally, or to detect groups of cancers or specific cancers. A multi-tiered assay may be developed using “general” regions (methylated in multiple cancers) and “specific” regions (methylated in only specific cancers). A multi-tiered test of this sort may be run together in one multiplex reaction, or may have its tiers executed separately.

Probes For Breast Cancer

Over 150 different PCR primer pairs were developed to the 52 different regions in the genome shown to exhibit extensive methylation in multiple breast cancer samples from the TOGA database but with no or minimal methylation in multiple normal tissues and in blood cells (Peripheral Blood Mononuclear Cells and others).

As proof of concept, these were then used to amplify bisulphite converted DNA from breast cancer cell lines MCF-7 (ER+, PR+), T47-D (ER+, PR+), SK-BR-3 (HER2+), MDA-MD-231 (Triple Negative) and normal breast lines MCF-10A and 184-hTERT. Sequencing adapters were added and Next Generation Sequencing carried out on an Ion Torrent sequencer. The sequencing reads were then separated by region and the sequence reads were analyzed using the BiqAnalyzer HT program.

Results

Example results of methylation analysis will be discussed herein. CHST11 is an example of a region methylated in prostate, breast, and colon cancer. FOXA is a region methylated in breast and prostate cancer. NT5 is a region methylated specifically in breast cancer.

FIG. 5 depicts sequencing results from a region from near the CHST11 gene (Probe C) is shown. For each cell line the results of a single sequencing read is depicted as a horizontal bar with each box representing a single CpG residue from between the PCR primers (in this case there being 6 CpG residues, Illustration at bottom right). Methylated bases are shown in dark grey while un-methylated bases are shown in light grey. Where a CpG could not be identified by the alignment program it is shown as a white box. Multiple sequence reads are shown for each cell line, stacked on top of each other. The numbers at the bottom of each stack indicates the number of sequence reads (Reads) and the overall methylation level determined from these reads (Meth).

When sequenced, these probes produced strong concordant signals that consisted of multiple methylated CpGs (5 to 25) where there is a strong correlation between individual sites being methylated in tumours. This eliminates false positive results due to PCR and sequencing errors. These tumour specific multiple methylated sites can be detected against a high background of normal DNA, being limited only by the read depth of the sequencing. Based on bioinformatic analysis of TCGA tumours, this essentially eliminates false positive signals.

FIG. 6 depicts results for CHST11 Probe A. Methylation in the region was characterized for a variety of breast cancer tumour samples (T) and in normal breast tissue samples (N) from the same patient. As in FIG. 5 the methylated bases are shown in dark grey while un-methylated bases are shown in light grey (illustration bottom left). Tumours of various subtypes were analysed including A02324 which is positive for HER2 amplification (HER2+), A02354 and B02275 which are Triple Negative Breast Cancer (TNBC), and D01333, D02291, D02610 which are all Estrogen and Progesterone Receptor positive tumours (ER+ PR+). The values below each column refer to the number of sequence reads obtained by Next Generation Sequencing (Reads) and the overall level of methylation of all of the CpG residues (Meth) based on these reads. Where no sequence reads were obtained for a given sample and box is shown as for sample D01333 N (Normal).

FIG. 7 depicts results of similar analysis of FOXA Probe A in breast cancer cell lines.

FIG. 15 depicts a numerical summary generated methylation data for prostate cell lines. # Reads is indicative of the number of reads exported, and Mean Me is indicative of the mean methylation.

FIG. 8 depicts results of similar analysis of the CHST11 Probe A and CHST11 Probe B in prostate cancer cell lines. DU145 is an Androgen Receptor (AR−) negative cell line which is able to generate metastases in the mouse. PC3 is also AR− and also metastatic. LNCaP is an Androgen Receptor positive line (AR+) which does generate metastases in the mouse while RWPE cells are AR+ and non-metastatic.

FIG. 9 depicts results of similar analysis of FOXA Probe A in prostate cell lines.

FIG. 10 depicts sequencing results to assess methylation status NET5 Probe E in breast cancer cell lines.

These results exemplify probes of differing specificities that can be selected using the approach outlined herein.

EXAMPLE 4

Probes For Uveal Cancer

Using the above-described methodologies, regions were selected for uveal cancer screening. Table 2 depicts these regions.

TABLE 2 Chromo- General some Start Stop Location Descriptor Size chr10 89611399 89611920 PTEN, KILLIN Shore CGI 521 chr11 35503400 35504124 PAMR1 small CGI 724 chr11 1.18E+08 1.18E+08 MPZL2 Prox Prom 599 chr15 60146043 60147120 C2CD4A Shore CGI 1077 chr17 24370858 24371386 SEZ6 small CGI 528 chr19 11060476 11060965 LDLR Prox Prom 489 chr2 1.66E+08 1.66E+08 GALNT3 CGI 1465 chr2 2.23E+08 2.23E+08 ccdc140/pax3 Shore CGI 4724 chr6 21774638 21775386 FLI22536/casc15 small CGI 748 chr6 24465699 24466545 KAAG1, DCDC2 CGI 846 chr6 31031220 31031651 MUC21 CGI 431 chr6 70632889 70633262 COL19A1 Proc Prom 373 chr6 1.09E+08 1.09E+08 NR2E1/OSTM1 small CGI 1001 chr7 29996242 29996333 SCRN1 Shore CGI 91 chr1 2450725 2452224 HES5 CGI 1499 chr1 12601228 12601893 DHRS3 Shore CGI 665

EXAMPLE 5

Tests For Breast Cancer Subtypes

The screen that has been described above, which originally incorporated all breast tumours in the TCGA database, can also be done on subsets of the tumour database.

BRCA1 carriers were taken out of the dataset and analyzed individually to identify target methylated regions specific to this subgroup. Breast cancer can also be divided in other ways: e.g., into five subtypes, Basal, Luminal A., Luminal B, HER2 and Normal-like. Patients in each of these groups were identified and analyzed to identify target methylated regions for each subset.

The screen can also be changed to look at individual patients using the previously described criteria to see who are positive or negative. Target methylated regions can then be ranked based on how many individuals are positive. This can help to remove biasing due to amalgamation (averaging). Targets can then be selected, e.g., if they are present in greater than 75% of patients for each subtype, and then rationalize amongst these.

Test For BRCA Carriers

Current monitoring practices for women at high risk of developing breast cancer due to familial BRCA1 or 2 mutations involve yearly MRI, however the high false positive rates result in a large number of unnecessary biopsies. Using the methodology described herein, a test may be developed to serve as a secondary screen, e.g., to be employed after a positive MRI finding; or to be used for primary screening of high risk patients. The blood test is designed to detect all types of breast cancer but because ER+ breast cancer is the most frequent it is biased towards these cancers, though some of the constituent probes do recognize HER2+ and TNBC tumours. In order to provide optimal sensitivity for the monitoring of BRCA1 and 2 an assay optimized for these patients may be developed.

Both TNBC and BRCA1 and 2 patients were selected from the TCGA 450 k methylation database. Generally, most BRCA1 and 2 tumours will present as TNBC but many non-familial cancers are also TNBC. These patients were analyzed using the above-described tumour specific methylation region protocol on both the overall TNBC population and on the BRCA1 and 2 patients. 85 tumour specific regions were identified for TNBC, 67 for BRCA1 and 13 for BRCA2 populations. Of these 39 were present in any two populations and they constitute the starting point for the development of this assay. Appropriate regions for a BRCA1 specific test were identified and assessed in individual patients with known mutations. This population is surprisingly uniform and most patients are recognized by a large number of probes. AUCs for individual probes are for the most part very high. Based on these results, an assay can be developed to detect all three, i.e., TNBC, BRCA1 and 2. If additional detection sensitivity is required, then individual tests can be constructed. For high risk women who are BRCA1 or 2 mutation carriers, their mutation status should be known so that the appropriate test can be applied.

Test For BRCA1 Carriers

Probes have been developed for the detection of cancer in carriers of the BRCA1 mutation. Methylation data from the TCGA Breast cancer cohort were selected from patients known to be carriers of pathogenic BRCA1 mutations. This data was then analyzed as described to identify regions of the genome specifically methylated in this sub-set of breast cancers. Table 3 lists appropriate regions identified and their genomic locations.

TABLE 3 Target Region (hg18 reference) chr Nearest Gene Start (nt) End (nt) Size chr1 LOC105378683 43,023,840 43,023,487 353 chr1 NPHS2 177,811,942 177,811,671 271 chr1 NR5A2 198,278,599 198,278,409 190 chr11 PAX6 31,783,955 31,782,545 1,410 chr11 KCNE3 73,856,332 73,855,762 570 chr12 KCNA6 4,789,491 4,789,342 149 chr12 TMEM132C 127,318,539 127,317,001 1,538 chr13 PDX1 27,390,265 27,389,540 725 chr13 EPSTI1 42,464,618 42,463,901 717 chr16 A2BP1 6,009,930 6,009,020 910 chr16 CRYM 21,202,914 21,202,448 466 chr16 PRKCB 23,755,504 23,754,826 678 chr16 IRF8 84,490,354 84,490,167 187 chr18 SALL3 74,842,145 74,839,705 2,440 chr19 LYPD5 49,016,848 49,016,696 152 chr2: DPP10 115,636,420 115,635,215 1,205 chr20 C20orf56 22,507,867 22,507,676 191 chr3 SOX2OT 182,919,993 182,919,839 154 chr4 CDKL2 76,774,880 76,774,658 222 chr5 March 11 16,233,072 16,232,633 439 chr5 CCL28 43,433,329 43,432,559 770 chr5 AP3B1 77,304,644 77,304,208 436 chr7 CARD11 3,050,299 3,049,859 440 chr7 BLACE 154,859,799 154,859,051 748 chr7 PTPRN2 157,176,806 157,176,096 710 chr8 RUNX1T1 93,183,481 93,183,326 155

52 different probes were then developed to various parts of these regions and the methylation pattern in tumor cell lines was characterized, including MDA-MB-436 and HCC1937 which are known to carry BRCA1 mutations. These probes will be combined with previously characterized probes to other regions which are also methylated in tumours from BRCA1 patients. This would provide for a highly sensitive assay able to detect cancer in these high risk women at the earliest possible stage.

Tests For Other Subtypes

A number of breast cell lines from women with known BRCA1 mutations have been isolated such as MDA-MB-436, HCC1937 and HCC1395 (all available from ATCC). These may be used to validate the assay as was done for the general blood test. For BRCA2 mutant lines there is only one ATCC cell line at present, HCC1937. There are several BRCA2 mutant ovarian cancer lines that have been identified and they may be used if the bioinformatic analysis confirms that these methylation markers are also found in ovarian cancer. The development of a single assay that detects both breast and ovarian cancer in BRCA2 carriers represents a distinct advantage as it would simultaneously monitor the two primary cancer risks in these patients.

The development of these assays follows the same course the above-described general assay proceeding from TCGA data to cells lines to patient samples. Tumour banks (some of which have mutation data) can be used for this, and analysis of these tumours provides an indication of their likely BRCA mutation. These samples can also be sequenced to confirm the prediction.

EXAMPLE 6

Testing of Cell-Free Samples

Proof of concept testing was carried out using cell lines for ease of analysis. However, the assay can be applied to test for cell-free DNA, e.g., circulating cell-free tumour DNA in blood, and finds wide application in this context. A sample protocol for circulating tumour DNA is provided.

Sample Protocol: Test For Circulating Tumour DNA

DNA Preparatio

The following example protocol may be used to detect circulating tumour DNA (tDNA).

Obtain DNA to be used for bisulfite conversion and downstream PCR amplification (i.e., cell line, tumour or normal DNA). Determine DNA purity on 0.8% agarose gel.

Determine genomic DNA (gDNA) for concentration in ug/uL by UV spectrophotometry.

Prepare a 1:100 dilution with TE buffer.

Remove RNA contaminates, if necessary, using the purification protocol for the GenElute Mammalian Genomic DNA Miniprep Kit, Sigma Aldrich, CAT #G1N350 (http://www.sigmaaldrich.com/technical-documents/protocols/biology/genelute-mammalian-genomic-dna-miniprep-kit.html). Follow purification protocol from steps A: 2a-3a, step 4-9.

OPTIONAL: For gDNA from a cell line, sonicate gDNA to approximately 90-120 bp (this represents general size of circulating tDNA). To do this, sonicate 5-10 ug of sample (50-100 ng/100 uL) using a sonicator. Use setting 4, and 15 pulses for 30 seconds with 30 seconds rest on ice in between. Determine sonicated DNA purity and bp size on 0.8% agarose gel.

Bisulfite convert DNA—EpiTect Fast Bisulfite Conversion Kit, QIAgen, CAT #59824 (https://www.qiagen.com/us/resources/resourcedetail?id=15863f2d-9d1c-4f12-b2e8-a0c6a82b2b1e&lang=en). Follow bisulfite conversion protocol on pages 1-18, 19-23. Refer to trouble shooting guide pages 30-32. Modifications to the protocol include: 1. Prepare reactions in 1.5 mL tubes, 2. High concentration samples at 2 ug, and low concentration samples at 500 ng-1 ug, 3. Perform the bisulfite conversion using 2 heat blocks set at 95° C. and 60° C., 4. Incubation at 60° C. extended to 20 minutes, to achieve complete bisulfite conversion, 5a Elute DNA in 10-20 uL of elution buffer for −50-100 ng/uL final concentration, and 5b Dilute DNA to 10 ng/uL for use in PCR.

Perform nested PCR with Hot Star Taq Plus DNA Polymerase, Qiagen, CAT #203605 (https://www.qiagen.com/ca/resources/resourcedetail?id=c505b538-7399-43b7-ad10-d27643013d10&lang=en).

Singleplex PCR Amplification

For singleplex PCR amplification of individual probes, carry out a primary PCR reaction with methylation-biased primers (MBP), (primer forward and reverse).

Table 4 recites reaction components.

TABLE 4 Component 1X (uL) 10X PCR Buffer 2.5 5 mM dNTP's 1 5 U Hot Star Taq 0.1 25 mM MgCl2 3 PCR Grade H2O 17 [10 ng/uL] DNA 1 10 pmol FWD Primer 0.2 10 pmol REV Primer 0.2 Total 25

Table 5 lists thermocycler conditions.

TABLE 5 Thermocycler Conditions Temp. Time 95° C. 15 min 95° C. 30 sec 58° C. 30 sec {close oversize bracket} X 40 72° C. 30 sec 72° C. 7 min  4° C. ∞

Carry out a secondary PCR reaction with universal primers CS1 (Barcode) and CS2 (P1 Adapter). To do this, remove an aliquot from the primary reaction, use as template DNA, this method serves as a two-step dilution PCR reaction

Table 6 recites reaction components.

TABLE 6 Component 1X (uL) 10X PCR Buffer 5 5 mM dNTP's 2 5 U Hot Star Taq 0.2 25 mM MgCl2 6 PCR Grade H2O 34.4 MBP PCR Template 2 10 pmol CS1 Primer 0.2 10 pmol CS2 Primer 0.2 Total 50

Table 7 recites thermocycler conditions.

TABLE 7 Thermocycler Conditions Temp. Time 95° C. 15 min 95° C. 30 sec 58° C. 30 sec {close oversize bracket} X 3 72° C. 30 sec 72° C. 7 min  4° C. ∞

Determine PCR specificity on 2% agarose gel. Run the methylation-biased PCR product and the CS1 CS2 sequencing PCR product beside one another on the agarose to visualize the banding pattern and increase in bp size. PCR product should be between 200-300 bp

For Singleplex PCR products, pool 5-10 uL of each PCR reaction (CS1 CS2 Secondary RXN) into a single tube for each sample type. Purify the pooled PCR with Agencourt AMPure XP beads at a 1.2:1 ratio (90 uL beads+75 uL sample), e.g., as below.

Agencourt Ampure XP Bead Purification

Use freshly prepared 70% ethanol. Allow the beads and pooled DNA to equilibrate to room temperature.

1. Add indicated volume of Agencourt AMPure XP beads to each sample: 90 uL beads+75 uL Pool (1.2:1)

2. Pipet up and down 5 times to thoroughly mix the bead suspension with the DNA. Incubate the suspension at RT for 5 minutes.

3. Place the tube on a magnet for 5 minutes or until the solution clears. Carefully remove the supernatant and store until purified library has been confirmed.

4. Remove the tube from the magnet; add 200 uL of freshly prepared 70% EtOH. Place the tube back on the magnet and incubate for 30 seconds; turn the tube around twice in the magnet to move the beads through the EtOH solution. After the solution clears, remove and discard the supernatant without disturbing the pellet.

5. Repeat step #4 for a second EtOH wash.

6. To remove residual EtOH, pulse-spin the tube. Place the tube back on the magnet, and carefully remove any remaining EtOH with a 20 uL Pipette, without disturbing the pellet.

7. Keeping the tube on the magnet, air-dry the beads at RT for ˜5 minutes.

8. Remove the tube from the magnet; add 50 uL of TE directly to the pellet. Flick the tube to mix thoroughly. Incubate at RT for 5 minutes.

9. Pulse-spin and place the tube back on the magnet for ˜2 minutes or until the solution clears. Transfer the supernatant containing the eluted DNA to a new 1.5 mL Eppendorf LoBind tube.

10. Remove the tube from the magnet; add 50 uL of TE directly to the pellet. Flick the tube to mix thoroughly. Store the beads, along with the supernatant, at 4° C. until purified library has been confirmed.

11. Visualize the sample pre- and post-purification on an 8% acrylamide gel (higher resolution). Pooled PCR product should be visualized as multiple bands (as each PCR product is a slightly different bp size). Purified sample should eliminate product beneath 150 bp.

FIG. 11 depicts a summary of BioAnalyzer electrophoresis summary for amplification product generated from various cell lines.

12. Perform nested PCR with Multiplex PCR Plus Kit, Qiagen, CAT #206152 (https://www.qiagen.com/ca/resources/resourcedetail?id=beb1f99e-0580-42c5-85d4-ea5f37573c07&lang=en), e.g., as below.

Multiplex PCR Amplification of Up to 50 Probes in a Single Reaction

Create multiplex primer mix by aliquot 1 uL of each forward and reverse primer at 10 pmol/uL into a single 1.5 mL tube. Calculate the final concentration of each primer by dividing the initial primer concentration by the final volume of primer mix in the tube, i.e., 15 probes to be multiplexed into a single reaction, would total 30 primers and at 1 uL each, 30 uL final volume. Thus ((10 pmol)(1 uL))/30 uL=0.333 pmol. Primer concentration requires optimization during PCR amplification, as the number of primers in a single reaction can influence the efficiency of the product, e.g.

15 primer sets ˜2 pmol final [ ] in PCR

50 primer sets ˜0.5 pmol final [ ] in PCR

Carry out primary PCR reaction with methylation-biased primers.

Table 8 lists reaction components for multiple amplifications of 15 probes, and Table 9 lists reaction components for multiple amplifications of 50 probes. Table 10 list reaction conditions.

TABLE 8 15 primer pairs at 2 pmol Component 1X (uL) 2X Multiplex MM 25 PCR H2O 18 Primer Mix 6 [10 ng/uL] DNA 1 Total 50

TABLE 9 50 primer pairs at 0.5 pmol Component 1X (uL) 2X Multiplex MM 25 PCR H2O 19 Primer Mix 5 [10 ng/uL] DNA 1 Total 50

TABLE 10 Thermocycling Conditions Temp. Time 95° C. 5 min 95° C. 30 sec 58° C. 90 sec {close oversize bracket} X 35 72° C. 90 sec 68° C. 10 in

Determine PCR specificity on 2% agarose gel. Multiplex products should be visualized with multiple banding pattern between 100-300 bp.

Pooling is not required for multiplex products, as the probes have already been combined and amplified into a single tube/reaction.

Purify the pooled PCR with Agencourt AMPure XP beads at a 1.2:1 ratio (60 uL beads+50 uL sample) (refer within document for purification protocol).

After PCR amplification, along with pooling and purifying, the samples can be quantified by qPCR, e.g., Ion Library Quantification Kit, TaqMan assay quantification of Ion Torrent libraries, Thermo Fisher Scientific, CAT #4468802 (https://tools.thermofishercom/content/sfs/manuals/4468986_IonLibraryQuantitationKit_UG.pdf)

1. Create a standard curve of 6.8 pM, 0.68 pM, 0.068 pM, 0.0068 pM

2. Dilute samples 1:1000, and run in duplicate

3. Perform qPCR assay on the Step One Plus Real Time machine by Life Technologies

4. Sample libraries quantified ≥100 pM can proceed to be sequenced on the Life Technologies Ion Torrent Sequencing platform

Life Technologies Ion Torrent PGM Sequencing

Ion PGM Template OT2 200.

Perform template reaction with Ion PGM Template OT2 200 Kit, Thermo Fisher Scientific, CAT #4480974. Kit contents to be used on the One Touch 2 and Enrichment system (https://tools.thermofisher.com/content/sfs/manuals/MAN0007220_Ion_PGM_Template_OT2_200_Kit_UG.pdf_(—)

Utilizing library quant. obtained from qPCR, dilute libraries appropriately to 100 pM. Follow Life Technologies guide on how to further dilute libraries for input into final template reaction.

Follow reference guide to complete template reaction

-   -   Run the Ion One Touch 2 instrument     -   Recover the template positive ISPs     -   Enrich the template positive ISPs with the Ion One Touch ES

Ion PGM Sequencing 200

Perform sequencing reaction with Ion PGM Sequencing 200 kit, Thermo Fisher Scientific, CAT #4482006. Kit contents to be used on the Ion PGM system (https://tools.thermofishercom/content/sfs/manuals/MAN0007273_IonPGMSequenc_200Kit_v2_UG.pdf).

Plan sequencing run

-   -   Select chip capacity (314, 316 or 318)     -   Determine sequencing flows and bp read length (i.e., 500 flows         and 200 bp read length)

Follow reference guide to complete PGM sequencing

-   -   Prepare enriched template positive ISPs     -   Anneal the sequencing primer     -   Chip check     -   Bind sequencing polymerase to the ISPs     -   Load the chip     -   Select the planned run and perform sequencing analysis

Sequencing data analysis and work flow

Obtain run report generated by the PGM and Torrent Browser

Run report includes the following information

-   -   ISP Density and loading quality     -   Total reads generated and ISP summary     -   Read length distribution graph     -   Barcoded samples: reads generated per sample and mean read         length

Obtain uBAM files generated by the PGM, available for download to an external hard drive

Bioinformatics data analysis

-   -   Upload uBAM files to a web based bioinformatics platform, Galaxy         GenAp         -   Perform quality control analysis (i.e., basic statistics and             sequence quality check)         -   Convert data files: BAM SAM FastQ         -   Filter FastQ file: select bp size to trim (i.e., trim             sequence <100 bp)         -   Convert data files: FastQ FastA         -   Download FastA file     -   Upload FastA files to BiqAnalyzer software platform         -   Create project         -   Add sample         -   Load reference sequence         -   Set gap extension penalty and minimal sequence identity         -   Link in FastA files to samples and reference sequences         -   Analyze and collect data files (pattern maps and pearl             necklace diagrams)

EXAMPLE 7

Uveal Melanoma Test

The molecular biology of uveal melanoma (UM) is simpler than that of breast cancer, with minimal mutations and rearrangements, and only two major sub-types which correspond to the retention or loss of chromosome 3p. A test was developed for UM which is superior to current state of the art blood assays.

Analysis of 450 k methylation TCGA data for 80 UMs allowed for the identification of regions of tumour specific methylation in both 3p- and 3pWT tumours using our algorithm. Table 11 shows 16 hypermethylated regions in both 3p- and 3pWT tumours used for probe development and testing, according to one embodiment.

TABLE 11 Gene Chr start stop Size CGI CpGs PTEN, KILLIN chr10 89611399 89611920 521 Shore CGI 171 PAMR1 chr11 35503400 35504124 724 small CGI 19 MPZL2 chr11 117640011 117640610 599 Prox Prom C2CD4A chr15 60146043 60147120 1077 Shore CGI 127 SEZ6 chr17 24370858 24371386 528 small CGI 34 LDLR chr19 11060476 11060965 489 Prox Prom GALNT3 chr2 166358156 166359621 1465 CGI 98 ccdc140/pax3 chr2 222881305 222886029 4724 Shore CGI 72 FLI22536/casc15 chr6 21774638 21775386 748 small CG 18 KAAG1, DCDC2 chr6 24465699 24466545 846 CGI 56 MUC21 chr6 31031220 31031651 431 CGI 46 COL19A1 chr6 70632889 70633262 373 Proc Prom NR2E1/OSTM1 chr6 108542808 108543809 1001 small CG 34 SCRN1 chr7 29996242 29996333 91 Shore CGI 133 HES5 chr1 2450725 2452224 1499 CGI 111 DHRS3 chr1 12601228 12601893 665 Shore CGI 133

The top 14 of these common regions were carried forward for probe development and a total of 26 different probes were characterized, with several regions having up to three probes targeting them. Each of these probes was then validated using six different UM cell lines to assess their methylation status. As negative controls, DNA from peripheral blood mononuclear cells (PBMCs), which are the main source of contaminating DNA in blood samples, as well as a pool of cell free DNA (cfDNA) from 16 individuals, were also tested (FIG. 15). These results indicated that the majority of the probes tested showed tumour specific methylation with little or no methylation in the negative controls. A total of 18 probes from 12 different regions were combined into a multiplex PCR reaction and used to analyze cell free DNA from plasma for a previously characterized cohort of metastatic UM patients.

The validated regions were C2CD4A, COL19A1, DCDC2, DHRS3, GALNT3, HES5, KILLIN, MUC21, NR2E1/OSTM1, PAMR1, SCRN1, and SEZ6. The validated probes were C2C5F, COL2F, DCD5F, DGR2F, GAL1F, GAL3F, HES1F, HES3F, HES4F, KIL5F, KIL6F, MUC2F, OST3F, OST4F, PAM4F, SCR2F, SEZ3F, and SEZ5F.

These patients were previously tested using the pyrophosphorolysis-activated polymerization (PAP) assay²⁶, which detects the frequent GNAQ or GNA11 mutations in UM²⁷. In all cases the test detected cancer in these patients even when the PAP assay failed to register a signal (FIGS. 16 and 17). Most of the probes functioned like methylation specific PCR reactions, only giving product when there was tumour DNA present though with the additional validation that the specificity of each probe was guaranteed by the presence of multiple methylated CpG residues within each read. In two patients from which serial blood samples were obtained (FIGS. 18A and 18B) the test showed increased tumour levels over time even when the final tumour volume was 0.5 cm³ (FIG. 18A). The test was also generally correlated with the volume of tumour, though the nature of the metastatic tumour as either a solid mass or dispersed has not yet been accounted for (FIG. 19). The levels detected by the test were generally in line with those of the PAP assay and notably gave a signal where PAP failed due to the lack of a mutation (FIG. 16, UM32). Where no or limited amounts of tumour DNA were detected by PAP, the test still gave significant signals (FIG. 20). Even greater sensitivity is expected when the total number of reads analyzed per patient is increased, as this run had less than optimal overall reads due to the presence of large amounts of primer dimer, an issue that has now been resolved. The specificity of the test was demonstrated by the extremely low levels of methylation seen in the pool of 16 cfDNA controls. Overall, the test has been validated in a patient population, and it has been shown to be superior to a state of the art mutation based assay.

EXAMPLE 8

Prostate Cancer Test

An important aspect of any test is that it should be applicable to all patients. Based on our experience it is essential to consider specific subtypes of a given cancer to ensure that all patients are detected by the assay. The TCGA analysis of a large prostate cohort revealed sub-groups based on specific mutations and transcriptional profiles²⁸. Four subtypes were identified based on the overall pattern of methylation found in these tumours. In this example the TCGA prostate cohort was divided into groups based on the methylation pattern and subjected to methylation analysis.

Table 12 lists 40 regions associated with all sub-types of prostate cancer.

TABLE 12 HES5 ANXA2 HLA-F HAAO LOC376693 RHCG PON3 RARB CSRP1 RARA LRRC4 ALDH1L1 ALOX5 PTRF HLA-J HIST1H3G PPM1H RND2 PAH ZSCAN12 MON2 TMP4 EPSTI1 HCG4P6 KIAA0984 HIF3A ADCY4 EYA4 TXNRD1 KLK5 HAPLN3 HOXA7 CHST11 AMOTL2 AX747633 HSF4 EFS SCGB3A1 NBR1 TMEM106A

These regions common to all four methylation subtypes were identified and a total of 38 probes from 33 regions were selected and appropriate “biased” PCR probes were generated. These were characterized using four different prostate cancer lines. DU145 is an androgen receptor (AR−) negative cell line that is able to generate metastases in the mouse. PC3 is also AR− and also metastatic. LNCaP is an androgen receptor positive line (AR+) that is non-metastatic in the mouse while RWPE cells are AR+ and non-metastatic. DNA from PBMC was also tested as this represents the primary source of cell free DNA in the circulation.

A total of 34 probes from 33 regions were validated in that they showed little or no methylation in PBMCs while showing large scale methylation in one or more of the tumour cell lines (FIG. 21).

The validated regions were ADCY4, ALDH1L1, ALOX5, AMOTL2, ANXA2, CHST11, EFS, EPSTI1, EYA4, HAAO, HAPLN3, HCG4P6, HES5, HIF3A, HLA-F, HLA-J, HOXA7, HSF4, KLK4, LOC376693, LRRC4, NBR1, PAH, PON3, PPM1H, PTRF, RARA, RARB, RHCG, RND2,TMP4, TXNRD1, and ZSCAN12.

The validated probes were ADCY4-F, ALDH1L1-F, ALOX5-F, AMOTL2-F, ANXA2-F, CHST11-F, EFS-F, EPSTI1-F, EYA4-F, HAAO-F, HAPLN3-F, HCG4P6-F, HES5-F, HIF3A-F, HLA-F-F, HLA-J-1-F, HLA-J-2-F, HOXA7-F, HSF4-F, KLK4-F, LOC376693-F, LRRC4-F, NBR1-F, PAH-F, PON3-F, PPM1H-F, PTRF-F, RARA-F, RARB-F, RHCG-F, RND2-F, TMP4-F, TXNRD1-F, and ZSCAN12-F.

To these 34 probes an additional 12 probes (from 7 regions) were added that had previously been characterized in breast cancer, which were also able to detect prostate cancer, for a total of 46 probes.

The added probes were C1Dtrim, C1Etrim, CHSAtrim, DMBCtrim, FOXAtrim, FOXEtrim, SFRAtrim, SFRCtrim, SFREtrim, TTBAtrim, VWCJtrim, and VWCKtrim.

These probes were multiplexed together and were then used to analyze plasma samples from five patients before they had initiated androgen deprivation therapy (ADT) and 12 months after starting treatment. These patients were part of a small cohort (˜40 patients) being followed for depression and the plasma samples at 0.5 ml were much smaller than normally used for the assay (2 mls). All of the patients were MO with no sign of metastatic disease when placed on ADT.

A variety of probes were positive depending on the particular patient (FIG. 22). The total number of positive probes was in keeping with the total number of methylated reads, which were normalized for total reads for each sample (FIG. 23). In all cases significant ctDNA signals were observed with results that were notably different than PSA results (FIG. 24). Two of the patients, TM19 and RM26 were started on ADT due to their aggressive diseases (T3A and T3B) despite having low PSA levels. PSA levels for both remained low but methylation detection of circulating tumour DNA (mDETECT) either decreased slightly (TM19) or rose dramatically (RM26) suggesting their diseases did not express PSA but had stable or increasing disease. HS29 showed decreased PSA levels which mDETECT paralleled. Both GL20 and GP27 trended in opposite directions to PSA levels with mDETECT increasing even with dramatic drops in PSA levels. GL20 did develop a radiation induced secondary cancer which may be what is detected. Ongoing analysis of additional clinical data is expected to help explain these results.

Based on the literature, three of these regions appear to have prognostic significance as well. C1orf114 or CCDC1 has been shown to be correlated with biochemical relapse. HES5 is a transcription factor that is regulated by the Notch pathway and methylation of its promoter occurs early in prostate cancer development. KLK5 is part of the Kallikrein gene complex that includes KLK3 (the PSA gene). We can demonstrate that KLK5 expression is correlated with methylation and KLK5 expression has previously been shown to be increased in higher grade tumours. These results strongly suggest that the examination of a large number of methylation markers may yield significant insight into the specific processes involved in prostate cancer development and produce diagnostic and prognostic information that would be vital for management of the disease.

EXAMPLE 9

Predictive Prostate Cancer Methylation Biomarkers

The 50 region assay according to embodiments described herein is sufficiently sensitive to easily detect metastatic disease and to follow changes in tumour size over time and, as indicated, has predictive value in itself. As described above, at least three regions, KLK5, HER5, and C1orf114 have potential to predict progression. In order to develop additional probes that are able to predict outcome in this patient population, the prostate cancer TCGA data was reanalysed to divide the patients by Gleason score. An inter-cohort comparison was conducted to identify regions frequently methylated in higher score cancers. Initially, Gleason grades 6 and 9 were compared as these typically represent less and more aggressive tumours and both groups had sufficient numbers of patients to ensure significance of the results. Probe development was carried out under the same criteria as with the original probe sets so that they could be used with ctDNA. No single probe will be absolutely specific for a given grade but a number of the probes showed excellent division between Gleason scores with the proportion of the cohort positive for a given grade increasing with increasing grade (FIG. 25). One of these, PSS3, is a gene whose expression has previously been associated with prostate cancer and particularly metastasis. It should be noted that not all methylation is associated with gene repression. Forty-three new probes were developed based on selection criteria to target the 36 regions shown in Table 13, which are associated with aggressive prostate cancer.

TABLE 13 ASAP1 EMX1 MIR1292 SOX2OT BC030768 HFE NBPF1 TUBB2B C18orf62 HIST1H3G/1H2BI NHLH2 USP44 C6orf141 HMGCLL1 NRN1 Intergenic (Chr1) CADPS2 KCNK4 PPM1H Intergenic (Chr8) CORO1C KJ904227 PPP2R5C Intergenic (Chr2) CYP27A1 KRT78 PRSS3 Intergenic (Chr3) CYTH4 LINC240 SFRP2 Intergenic (Chr4) DMRTA2 Me3 SLCO4C1 Intergenic (Chr10)

The probes were ASAP1/p, BC030768/p, C18orf62/p, C6orf141/p-1, C6orf141/p-2, CADPS2/p, CORO1C/p-1, CORO1C/p-2, CYP27A1/p, CYTH4/p, DMRTA2/p, EMX1/p, HFE/p-1, HFE/p-2, HIST1H3G/1H2BI/p, HMGCLLI/p, KCNK4/p, KJ904227/p, KRT78/p, LINC240/p-1, LINC240/p-2, Me3/p-1, Me3/p-2, MIR129, NBPF1/p, NHLH2/p, NRN1/p, PPM1H/p-1, PPM1H/p-2, PPP2R5C/p, PRSS3/p, SFRP2/p-1, SFRP2/p-2, SLCO4C1/p, SOX2OT/p, TUBB2B/p, USP44/p, Chr1/p-1, Chr2/p-1, Chr3/p-1, Chr4/p-1, Chr8/p-1, and Chr10/p-1.

It is expected that it will be an overall pattern of hypermethylation, rather than a single probe, that will have the greatest predictive power.

EXAMPLE 10

Breast Cancer Test

One approach described herein for identifying hypermethylated regions in breast cancer focused on the most frequently methylated regions within the TOGA database. Due to the large number of LumA and LumB patients in this dataset there was a significant under-detection particularly of the Basal class of tumours.

Accordingly, the data were reanalyzed based on the four molecular subtypes LumA, LumB, Her2 and Basal. The Normal-like subtype is not very frequent in the dataset and as expected is very close to normal tissue, however a small number of regions recognizing this subtype were also included. Overall, methods and probes were developed and tested for over 230 different regions (some with multiple probes), and these have been validated using a variety of breast cancer cell lines and tumour samples. Some regions are subtype-specific but most recognize multiple subtypes. These have been assembled into a single test incorporating 167 different probes which recognize all subtypes (FIGS. 26A, 26B, and 26C), with all patients being recognized by a significant number of probes. By looking at just the top 20 probes for each subtype this test has an area under the curve (AUC) per subgroup from 0.9078 to 0.9781, indicating that high detection rates have been achieved for all types of tumours (FIG. 27). This also means that the test is able to identify the subtype of tumour based on the distribution of probe methylation.

Another test specific for the triple negative breast cancer (TNBC) subtype was developed from the larger set of general regions identified as described above. This test incorporates 86 probes from 71 regions, listed in Table 14.

TABLE 14 CCL28 PTPRN2 UDB IRF4 HOXA9 HINF1B POU4F1 PAX6 BARHL2 TMEM90B SOX2OT NT5E TNFRSF10D VWC2 PPFIA3 PRSS27 C1orf114 TSPAN33 DPP10 CD38 BRCA1 SPAG6 DMRTA2 ITPRIPL1 CA9 FOXA3 CHST11 HOXB13 TMEM132C NR5A2 GIPC2 IRF8 C5orf39 FABP5 OTX2 DMBX1 BOLL ERNA4 CRYM PTGDR Intergenic5 TAL1 SLC7A4 MAST1 GNG4 SALL3 EVX1 TOP2P1 LEF1 DRD4 DDAH2 ID4 ACVRL1 PRDM13 CARD11 Intergenic 8 EPSTI1 GABRA4 TBX15 GALR3 NFIC TCTEX1D1 TTBK1 PRKCB ALX1 CDKL2 PDX1 PHOX2B SCAND3 NPHS2 SIM1

The probes were ALX1, AVCRL1, BRCA1-A, C1Dtrim, C1Etrim, CA9-A, CARD11-B, CCL28-A, CD38, CDKL2-A, CHSAtrim, CRYM-A, DMBCtrim, DMRTA2exp-A, DPP10-A, DPP10-B, DPP10-C, DRD4-A, EFNA4-B, EPSTI1, EVX1, FABP5, FOXAtrim, FOXEtrim, GALR3-A, GIPC2-A, HINF C trim, HOXAAtrim, HOXACtrim, HOXB13-A, Int5, Int8, IRF8-A, ITRIPL1, LEF1-A, MAST1 A trim, mbBARHL2 Trim, mbBOLL Trim, mbC5orf Trim, mbDDAH Trim, mbDMRTA Trim, mbGABRA A Trim, mbGABRA B Trim, mbGNG Trim, mbID4 Trim, mbIRF Trim, mbNT5E Trim, mbSIM A Trim, mbTBX15 Trim, NFIC-B, NFIC-A, NPSH2-B, NR5A2-B, OTX2-A, PAX6-A, pbDMRTA Trim, pbGNG Trim, pbSCAND Trim, pbTAL Trim, PDX1exp-B, PHOX2B-A, POU4F1 A trim, PPFIA3-A, PRDM13, PRKCB-A, PRKCB-C, PRSS27-A, PTGDR, PTPRN2-A, PTPRN2-B, SALL3-A, SALL3-B, SLC7A4-A, SOX2OT-B, SPAG6 A trim, TCTEX1D1-A, TMEM-A, TMEM-B, TMEM90B-A, TNFRSF10D, TOP2P1-B, TSPAN33-A, TTBAtrim, UBD-A, VWCJtrim, and VWCKtrim.

The ability of this test to detect TNBC was validated by the analysis of 14 TNBC primary tumours as well as matched normal tissue from four of these patients. Large scale methylation was observed for the majority of probes and was distinctly different from the normal samples (FIG. 28).

EXAMPLE 11

Sensitivity of the Tests

The tests described herein are designed to detect less than one genome's worth of DNA in a sample through the use of multiple regions where a single probe out of many can signal the presence of a tumour. The more regions and probes incorporated into a test the greater is the sensitivity. This is in contrast to mutation detection where the presence of a single mutation per genome equivalent means that random sampling effects rapidly limit sensitivity when the concentration of the tumour DNA falls below one genome equivalent per sample. The presence of large amounts of normal DNA in fluid samples also creates problems for the detection of mutations through the relatively high error rates for PCR and sequencing. To assess the limits of methods and tests described herein, a dilution experiment was performed wherein DNA from a TNBC cell line (HCC1937 DNA) was diluted into a constant amount of PBMC DNA (10 ng) from a normal patient (FIG. 29). These samples were then tested using the TNBC test. A conclusive signal was obtained from the test even when as little as 0.0001 ng of TNBC DNA was present in 10 ng of PBMC DNA. This represents a detection of 0.03 genome equivalents of tumour DNA against a background of 100,000 times more normal DNA.

EXAMPLE 12

Discussion

The sensitivity of mutation based detection tests is limited by their detection of single unknown mutations in genes, such as p53 or ras. As only a single mutation is present per genome equivalent, this dramatically limits the sensitivity of these assays. Once the concentration of tumour DNA in the blood decreases to less than one genome equivalent per volume of blood analysed, the probability of detecting a mutation decreases dramatically as that particular segment of DNA may not be present in the blood sample. The assay described herein incorporates multiple probes for multiple regions from across the genome to dramatically increase sensitivity. For example, up to 100 or more probes may be incorporated into the assay, making it up to 100 or more times more sensitive than mutation based tests.

Circulating tumour DNA may be produced by the apoptotic or necrotic lysis of tumour cells. This produces very small DNA fragments in the blood. With this in mind, PCR primer pairs were designed to detect DNA in the range of 75 to 150 bp in length, which is optimal for the detection of circulating tumour DNA.

The use of DNA methylation offers one more advantage over mutation based approaches. Mutated genes are typically expressed in the cells (such as p53). They are thus in loosely compacted euchromatin, in comparison to methylated DNA which is in tightly compacted heterochromatin. This methylated and compacted DNA may be protected from apoptotic nucleases, increasing its concentration in the blood in comparison to these less compacted genes.

Extensive analysis of the genome wide methylation patterns in breast, colon, prostate and lung cancers and normal tissue in each of these organs based on TCGA data was carried out. 52 regions were identified for breast cancer which fulfill design criteria, which looks for an optimal difference in methylation between tumour and normal breast tissue, and where there is no methylation in any of the other normal tissues. As well, there should optimally be at least 2 CpG residues within 200 basepairs of each other. This ensured that regions of coordinated tumour specific methylation have been identified.

Within these 52 regions, 17 were found in common with colon cancer, and 9 in common with prostate cancer. Interestingly there were few appropriate regions identified in lung cancer, with only 1 overlapping with breast cancer. Most of these regions are associated with specific genes, though several are distantly intergenic, and almost all were found in CpG islands of various sizes. Probes were first developed for those regions with some commonality between cancers and designed PCR primers which recognize the methylated DNA sequence. This provides a bias in the amplification process for tumour DNA, enriching the tumour signal. These primer pairs amplify regions of 75 to 150 bp in accordance with our design criteria. Typically these regions contain from 3 to 12 CpG residues each, ensuring a robust positive signal when these regions are sequenced. Multiple non-overlapping probes were used as the CpG islands are generally larger than 150 bp, allowing for multiple probes for each appropriate region, providing more power to detect these regions and increasing the detection sensitivity of the assay.

Six different breast cancer lines were used in this validation analysis that have been shown to generally retain tumour specific methylation patterns²². MCF-7 and T47D lines are classic ER+positive cell lines representing the most frequent class of breast cancer. SK-BR-3 cells are a HER2+ line and MDA-MB-231 cells represent a Triple Negative Breast cancer (TNBC), thus the 3 main categories of breast cancer are represented covering 95% of all tumours. Two “normal” lines were also used, the MCF10A line, though this line has been shown to contain some genomic anomalies, and the karyotypically normal 184-hTERT line. DNA was bisulphite converted, and the probes were amplified individually, barcoded then pooled according to cell line and subject to Next Generation Sequencing on an Ion Torrent sequencer. Not all PCR primer pairs produced a product due to the methylation-based nature of the primers, but in general, where a signal was detected, around 1000 reads were obtained per probe for each cell line. These reads were processed through our NGS pipeline using Galaxy and then loaded into the NGS methylation program BiqAnalyzer^(23,24). This program extracts probe specific reads, aligns them against the probe reference sequence, and calls methylated and unmethylated CpGs. It also carries out quality control measures related to bisulphite conversion and alignment criteria. In all of these probes there are several CpG residues within the primer sequence producing a bias towards amplifying methylated DNA. The analysis shown only includes CpGs outside of the primers which are solely representative of the methylation status of the sample being analysed.

FIGS. 5 and 6 depict results for the CHST11 gene, which is a good example where robust PCR primers are able to recognize tumour specific methylation. Four different primer pairs were assessed, three of which amplify probes that partially overlap. In all four cases these regions are completely methylated at all CpGs (not including CpGs in the primers) and are essentially completely unmethylated in the normal lines. CHST11 primers do not recognize the Her2 or TNBC lines, but other primers such as ADCY and MIRD do. The corresponding probes cover a small region of the CpG island and information about the status of the rest of the CpG island is limited due to the relatively coarse resolution of the 450K methylation data. Clearly the remaining part of the CpG island can be developed for additional probes that would increase the sensitivity of detection.

FIG. 7 shows that FOXA probe A had similar characteristics and recognized all but one TNBC tumour. This proves that the target and probe development pipeline moving from TCGA data to cell lines and then to patient normal and tumour tissue successfully identified primer pairs that are able to specifically recognize tumour DNA based on their methylation patterns.

Validation work continues to validate potential probe regions. A further 24 regions were characterized using 52 different probes in the cell lines as an initial screen for their suitability.

FIG. 4 shows the results of analysis of all of the potential CpGs identified in the TCGA cohort for individual patients indicates most patients are recognized by a large proportion of these probes.

FIG. 3 shows the results of ROC analysis²⁵ and indicates each of these probes has a very high AUC, suggesting excellent performance individually and presumably even better when combined.

It has been noted that there does appear to be a population of patients with relatively few positive probes. This is not subtype specific and other probes specific for this population have been identified. As appropriate, additional probes will be developed for all suitable regions and expanded to include other parts of the associated CpG islands. Overall it is expected that 100-150 separate probes in the assay will provide optimal sensitivity.

FIGS. 12A and 12B depict a numerical summary of validation data, wherein “# Reads” indicates the number of reads, and “Mean” Me indicates the mean methylation observed in results. Approximately half of the probes met the design criteria of having complete methylation of all CpG residues in the tumour samples and little or no methyation in the normal lines.

The next step in validating each of these probes was to examine their methylation patterns in actual patient tumour samples. A small cohort of patient samples was used to investigate GR methylation. From this group three ER+ tumours (one of which is positive for GR methylation), one HER2+ tumour and two TNBC tumours were chosen, as well as their corresponding normal controls. Taking the CHST11A probe as an example, FIG. 6 shows that all six of the normal breast tissue samples had either no reads due to the methylation biased amplification yielding no product or minimal methylation. In no case was there any concerted methylation signal where all CpGs were methylated. In contrast, in one TNBC and one ER+PR+ tumour a strong concordant methylation signal was seen at all six CpG sites. The other 2 ER+PR+ tumours also showed consistent methylation at four or five CpGs with their normal breast tissue controls having minimal reads with only one CpG showing any methylation.

FIGS. 13A and 13B depict a numerical summary of generated methylation data for tumour samples for all probes tested to date. # Reads is indicative of the number of reads exported, and Mean Me is indicative of the mean methylation.

Initial proof of concept work involved mixing experiments where non-methylated and methylated DNA was mixed in increasing ratios. This demonstrated that based in the presence of multiple CpG signatures methylated DNA could easily be detected in the presence of at least a 500 fold excess of unmethylated DNA. These probes were amplified with PCR primers that were not methylation specific or biased, and the probes developed to date do incorporate a bias towards methylated DNA, which further increases the detection sensitivity. However, they do amplify non-methylated DNA (in part because primers were designed with no preference as to the location of methylation sites within the primers). This was done intentionally as it provides for a potential quantitative aspect to this assay. Some of the circulating normal DNA in blood samples is likely from the lysis of nucleated blood cells, which is why serum is preferred over plasma as a source of DNA. However the ratio of tumour to normal DNA in blood may provide some quantitation of the actual concentration of tumour DNA present in the blood, which is thought to be correlated with tumour load. Since tumour can be distinguished from normal DNA reads, the ratio between them can be used as a proxy for the tumour DNA concentration. The number of tumour specific reads per volume of blood, regardless of the number of normal reads, may also prove to be closely linked to circulating tumour DNA levels.

Optimizing this test may include multiplexing to allow all of the probes the opportunity to amplify their targets in a given sample of DNA. Through the use of limited concentrations of primers and cycles, excellent amplification of all probes was obtained within a set of 17 primer pairs. Expanding this to include all of the optimized primers is not expected to be an issue.

The test may be implemented as a blood based breast cancer detection system in patient blood samples.

Based on development and validation work to date, the assay offers significant advantages other current and developing tests based on sensitivity, specificity, and detection sensitivity.

Some potential applications of the embodiments described herein are listed below by level of detection sensitivity:

Determining response to neo-adjuvant chemotherapy;

Monitoring tumour load in diagnosed patients;

Detecting residual disease post-surgery;

Detecting relapse;

Secondary screen after positive MRI in high risk patients;

Direct monitoring of high risk patients; and

Primary population screening.

The analysis of patients with active breast cancer offers the ability to assess a number of different aspects of this blood based test. Patients with locally advanced disease can be recruited preferentially, as these patients generally have larger tumours, receive neo-adjuvant therapy, are more likely to have residual disease and are at higher risk of relapse. By analysing blood samples from these patients upon diagnosis, after any neo-adjuvant treatments, pre-surgery, and at followup visits post-surgery it is possible to follow the relative tumour burden in these patients over the course of treatment. This will allow the tumour size and type to be correlated with the results of the test described herein.

Patients can be recruited in the clinic after a biopsy confirmed positive diagnosis. Blood can be drawn in conjunction with other routine blood work at diagnosis, after neo-adjuvant treatment, before surgery, within a month after surgery and every 3-6 months following that. Blood from 50 aged matched women without disease can also be collected from the community to provide control samples for the patient cohort. Relevant clinical data can be collected including radiological assessments and/or pathology reports. In particular, the receptor status of the tumours, the size of the tumour based on both radiological assessment and examination of the excised tumour, as well as treatments and response to therapy can be correlated with the circulating DNA analysis.

The assay described herein is expected to be quantitative at different levels. At very low levels of tumour DNA, the random presence of the tumour DNA in a sample will result in a subset of individual probes being positive, with the number of positive probes increasing with greater tumour DNA levels. At higher levels of tumour DNA the number of tumour specific reads will increase, either as an absolute number or in relation to the number of normal DNA reads. As a result methylation data can be treated in three ways:

(1) As a binary outcome where each probe will be considered to be positive if it has any tumour specific methylation pattern present;

(2) An individual threshold of methylation will be established for each probe based on the minimum number of reads required to call a tumour; or

(3) Tumour specific reads per number of normal reads for each probe (or, e.g., per 100,000 total reads).

Each of these approaches may be used to carry out logistic regression on the patient and control sets. Receiver Operating Characteristic (ROC) analysis may be used to define thresholds for each probe that maximizes the sensitivity and sensitivity of the assay. The performance of the entire assay may be characterized using Area Under the Curve (AUC) analysis for overall sensitivity, specificity, classification accuracy and likelihood ratio. Pearson or Spearman correlations may be used to compare patient parameters with the test outcomes.

Changes in methylation may be important drivers of breast cancer development and that these occur very early during the process of transformation. This may explain why many of the observed methylations are common amongst different breast cancer sub-types, while some are even common to other cancers. This may mean that these changes predate the development of full malignancy and suggests that they could also have value in assessing the risk of a women developing breast cancer. It is envisaged that the assay described herein can be used to track the accumulation of risk in the form of increasing gene specific methylation levels and could be used to develop a risk assessment tool. This would be useful for the development and assessment of risk mitigation and prevention strategies.

Table 15 lists the primers used herein for each probe.

TABLE 15 PCR 5′-3′ Primer Sequence Product Gene Probe (Bisulfite) Chr: Location Length C1orf114/ C1Df TTGAGGTAAAGGAGATTTCGGT chr1: 167663228- 134 CCDC18 C1Dr ACATACGCCTACGCAAATTTTTA 167663361 C1Ef TTCGGTGTTTGCGAAGGGTTA chr1: 167663398- 111 +C1Er TCACAACCAACACAACGACACTT 167663508 C1Er ACAACCAACACAACGACACTT C1Ff TCGGTATTTGTTTTCGCGGT chr1: 167663245- 112 C1Fr CGCCTACGCAAATTTTTATCGC 167663356 C1Gf CGAGAGCGATAAAAATTTGCGT chr1: 167663330-  88 C1Gr ACCCTTCGCAAACACCGAAA 167663417 C1 eAf GGTAATAGCGTGTTTTTGC chr1: 167663285-  82 C1 eAr ATATTACATACGCCTACGCAAA 167663366 C1 eBf TTTGTGTAAAATGCGGCGGT chr1: 167663149- 118 C1 eBr CTACCGCGAAAACAAATACCGA 167663266 C1 eCf ATTTCGGTGTTTGCGAAGGG chr1: 167663395- 112 C1 eCr ACAACCAACACAACGACACT 167663506 VWC2 VWCJf TTTCGGTTGTCGGGTTTGGA +VWCJf TATTTCGGTTGTCGGGTTTGGA chr7: 49783871- 133 VWCJr CCCTCAATCGCTCATCCTCC 49784003 VWCKf TCGTCGGTCGGTTTAGGATG chr7: 49784151- 129 +VWCKr AAAACCGACGCCAAACCTACAT 49784279 VWCKr AACCGACGCCAAACCTACAT VWCLf CGGAGGATGAGCGATTGAGG chr7: 49783983- 118 VWCLr TAACGCGCACACCGAACTAA 49784100 VWCMf CGAGTTGGGGTCGCGATTAT chr7: 49784021- 150 VWCMr CATCCTAAACCGACCGACGA 49784170 VWCNf CGACGCGTTACGGTTGTTTA chr7: 49783849- 125 VWCNr CCGCTTCTCCGAAACCAAAC 49783973 VWC2 eAf TAAGGCGGGGTTTTTAGAGC chr7: 49783687- 106 VWC2 eAr TAAAAACTAACGCGCCCG 49783792 VWC2 eBf GGTTTCGGTGTTATTCGC chr7: 49783797- 126 VWC2 eBr CTCCTCTCCGCGAAAAAAT 49783922 VWC2 eCf CGGAGGATGAGCGATTGAGG chr7: 49783983- 118 VWC2 eCr TAACGCGCACACCGAACTAA 49784100 VWC2 eDf TCGTCGGTCGGTTTAGGATG chr7: 49784151- 127 VWC2 eDr AACCGACGCCAAACCTACAT 49784277 VWC2 eEf GTCGGACGCGTTTTAGTTGG chr7: 49784315- 110 VWC2 eEr TCCCTACCGACCTCAACACT 49784424 MIR129-2 MIRBf TGGTTGGGGGATTTTGAGGG chr11: 43559089- 141 MIRBr AAACCTCCCCGCCTACCTAT 43559229 MIRCf GCGGACGGTTTGGAGAAATG chr11: 43559343-  82 MIRCr CGCGACTCAATCTCACCACT 43559424 MIRDf GGAGGTTGGGTTTCGGGATT chr11: 43559257- 127 MIRDr GCGCCCCTAAACTCGTATCT 43559383 MIREf GCGGAGTGGTGAGATTGAGT chr11: 43559401- 113 MIREr ACCGACTTCTTCGATTCGCC 43559513 MIRFf ATAGGTAGGCGGGGAGGTTT chr11: 43559205- 139 MIRFr CGATCCCCCAACTCAACCC 43559343 MIR eAf TGAGTTGGCGGTTTCGTTTG chr11: 43559004- 122 MIR eAr CCCGAATCCCCTCTTATCCC 43559125 MIR eBf CGCGATTTTGTAGTCGGGGT chr11: 43559156-  96 MIR eBr TTTCCTATCGCCCCAACACC 43559251 MIR eCf GGAGGTTGGGTTTCGGGATT chr11: 43559257- 127 MIR eCr GCGCCCCTAAACTCGTATCT 43559383 MIR eDf GATTGAGTCGCGATGGAACG chr11: 43559413-  81 MIR eDr GCCGCCTTCAACCCAAAATA 43559494 ADCY4 ADCYFf CGCGAGCGTATAGAGTACGA chr14: 23873573 163 ADCYFr ACCCTAACCAACCCCGAAAC 23873735 ADCYGf TAGCGTCGCGAGCGTATAGA chr14: 23873567- 188 ADCYGr AAAAATAACCCGACGCCCGA 23873754 ADCYHf GGTTTCGTAGAAGAGGTTTTC chr14: 23873642- 174 ADCYHr CGCGAAATAATAACGACTTT 23873815 ADCY4 eAf AGAAGAGGTTTTCGTTGGGGG chr14: 23873650-  80 ADCY4 eAr ACCAACCCCGAAACTCGAAA 23873729 ADCY4 eBf TAGGATTTGGGGTTGGTGCG chr14: 23873975- 141 ADCY4 eBr AACGCAACGACGAACGTAAC 23874115 ADCY4 eCf TGGTAGTGGGGAGATCGAGG chr14: 23874376-  99 ADCY4 eCr AAACGCCCCCAACTCTAACC 23874474 DMBX1 DMBAf GTTGCGGACGGCGTAGAT chr1: 46723984- 149 DMBAr ACGCTCCCCGAAACAATAACT 46724132 DMBBf TTGTTAGTTTTGTTAGCGCGG chr1: 46723919-  75 DMBBr CGTCCGCAACGATTCATCATC 46723993 DMBCf TGTTTAGGAGATGGTTCGTGGT chr1: 46723889- 115 +DMBCr GCATCTACGCCGTCCGCAAC 46724003 DMBCr ATCTACGCCGTCCGCAAC DMBX1 eAf TGTTTAGACGTGGGTTGGGG chr1: 46723237-  87 DMBX1 eAr TCAACTCCACTCACCCCGTA 46723323 DMBX1 eBf GAGGAGGGTGGAGAGGGTAG chr1: 46723478- 133 DMBX1 eBr ATACCGCACGTACTCCCAAC 46723610 DMBX1 eCf GGAGTGGAGTAGGTAGCGGT chr1: 46723635- 117 DMBX1 eCr TTCCTAACCCTCTCCGACCA 46723751 DMBX1 eDf TTTTTGAGCGGTGAAGGGGA chr1: 46723764- 125 DMBX1 eDr AATTATTAACGCGACCGCCG 46723888 HOXA9 HOXAAf GTAATAATTTGGTGGTATCGGGGG chr7: 27171666- 100 HOXAAr TCTACTAAACGAACACGTAACGC 27171765 HOXABf ATAATTTGGTGGTATCGGGGG chr7: 27171669- 109 HOXABr ACGCGTTATTATTCTACTAAACGAA 27171777 HOXACf TGGGGTTTGTTTTAATTGTGGTT chr7: 27171878- 152 +HOXACr GCGAAACCCGCGCCTTCTTAAT 27172029 HOXACr GAAACCCGCGCCTTCTTAAT HOXADf GGGGAAGTATAGTTATTTAATAAGTTG chr7: 27171688- 128 HOXADr ACAAAACATCRAACCATTAATAA 27171815 HOXA9 eAf TTCGCGAAGGAGAGCGTATC chr7: 27171234- 101 HOXA9 eAr CCCTACGTACACCCCCAAAC 27171334 HOXA9 eBf CGTTTGGGGGTGTACGTAGG chr7: 27171314-  88 HOXA9 eBr AAACCCAATACACGCGACGA 27171401 HOXA9 eCf TTTGTCGGGGAGGTTGGTTT chr7: 27171478-  82 HOXA9 eCr TTCCTACTAAACGCCGACGC 27171559 HOXA9 eDf TAGCGTTTGGTTCGTTCGGT chr7: 27171611- 123 HOXA9 eDr ATAAAAACGCGAACGCCGAC 27171733 SFRP5 SFRAf GCGGGCGTTTCGATTGATTT +SFRAf TTGCGGGCGTTTCGATTGATTT chr10: 99521730- 131 SFRAr TAAAAACCGCCCCCACTACC 99521860 SFRBf TGTTCGGCGGTTTAGGTGTT chr10: 99521628- 124 SFRBr AAATCAATCGAAACGCCCGC 99521751 SFRCf TAGTTCGGGTTTCGTCGTGC chr10: 99521776-  90 +SFRCr AAAACTAAAAACCGCCCCCACT 99521865 SFRCr AACTAAAAACCGCCCCCACT SFRDf GTGGGTGGTAGTTTGCGTTG chr10: 99521713- 135 SFRDr CACTACCTCCCCGCCTTAAA 99521847 SFREf GCGTGCGTTTTCGGTTTTGA +SFREf CGGCGTGCGTTTTCGGTTTTGA chr10: 99521649-  83 SFREr AACGCAAACTACCACCCACC 99521731 SFRP5 eAf GGACGTTGGGTTGAGTTAGGA chr10: 99520910- 109 SFRP5 eAr ACGACCCTACAACTCCCCTA 99521018 SFRP5 eBf GGTGTTCGAATTGTACGGCG chr10: 99521073- 107 SFRP5 eBr CTACGCGCCGCTCATAAAAA 99521179 SFRP5 eCf GCGCGTACGGTTTCGTATAG chr10: 99521183-  75 SFRP5 eCr ATACTCGCTCTTTACGCCCG 99521257 SFRP5 eDf TAGAGCGGTAGGTCGGTAGG chr10: 99521393-  79 SFRP5 eDr AACAAACCGAACCGCTACAC 99521471 CHST11 CHSAf GCGGCGTGGGAATGAATTTT +CHSAf GGGCGGCGTGGGAATGAATTTT chr12: 103376278- 120 CHSAr CTTTCCCTCGCACCCCTAAA 103376397 CHSBf TGCGAGGGAAAGTTTGGGTT chr12: 103376386- 123 CHSBr CCGCGTTACCCGAAAAACTT 103376508 CHSCf TTTTAGGGGTGCGAGGGAAA chr12: 103376377-  86 CHSCr CGCAACCGAACTACTCACCC 103376462 CHSDf GTGCGAGGGAAAGTTTGGGT chr12: 103376385- 126 CHSDr ACCCGCGTTACCCGAAAAA 103376510 CHST11 eAf TTTTTTTGGTTGTCGGGTC chr12: 103375901- 109 CHST11 eAr CGAAACCCGAAACACGTA 103376009 CHST11 eBf AGAGTGGTCGGGTGTTTAGC chr12: 103376031- 149 CHST11 eBr ACGTAACCCAAAAACTCGAAA 103376179 CHST11 eCf GTCGTTTTTTAGGGGTGC chr12: 103376371-  99 CHST11 eCr TAAACTTCGCAACCGAACTA 103376469 CHST11 eDf TATTAAGTTTGCGTTTGGGTC chr12: 103376781- 109 CHST11 eDr AAAACCGTCTATCCCTACGC 103376889 FOXA3 FOXAf CGAGGTAGGAAGTTTTGCGG chr19: 51071936- 103 FOXAr CGACTCCTCCCGCGAAATAA 51072038 FOXBf CGGGGTGTTGTTGTAGGGTT chr19: 51072158-  93 FOXBr AATCACACCTACCCACGCC 51072250 FOXCf TAGGGCGGTTAGGTTTGGGG chr19: 51072076- 128 FOXCr GACGAATAACCCCACCCTCC 51072203 FOXDf TTGTCGCGTTGGTTTTTCGT chr19: 51071765- 103 FOXDr ACCTTTCTCTCGACCCCAAT 51071867 FOXEf CGTTTTGTCGGTTGCGTGTTA chr19: 51071734-  91 FOXEr ATTCCCCGACCTACCCAAAAC 51071824 FOXA3 eAf GGTAGGTGATAACGTTAGTGGGTT chr19: 51068615- 110 FOXA3 eAr ACCTCCATCCCCTACCCAAC 51068724 FOXA3 eBf AGTAGGGGGAGGTGGTTTTG chr19: 51069110- 135 FOXA3 eBr TCCTCCTCCCCAACTTAACC 51069244 FOXA3 eCf AGTTTGGGTGTGGCGGTTTA chr19: 51070046- 111 FOXA3 eCr ACCAACTTCGCCATATTAACCA 51070156 TTBK1 TTBAf CGCGGTGTATTGTGGGTAGT chr6: 43319189-  99 TTBAr CCTTCCGACCCGAATCATCC 43319287 TTBBf GGTCGTCGGAACGTGATGT chr6: 43319101-  86 TTBBr GCCAACATCAACACCAACCC 43319186 TTBCf TCGTTTTGTCGTTGTCGTCG chr6: 43319212- 107 TTBCr TTAAATAACCCGCTCCCTCCG 43319318 TTBDf GTCGTGATGTTAGAGCGGGC chr6: 43319130- 126 TTBDr ACCCCGATCCTCCTTAAACG 43319255 TTBK1 eAf TTAAGGAGGATCGGGGTC chr6: 43319239-  91 TTBK1 eAr TCAATACGACGTTAAATAACCC 43319329 TTBK1 eBf TGGAGTTAAGCGGGTGGTAG chr6: 43319008- 141 TTBK1 eBr CCCGCTCTAACATCACGACTC 43319148 TAL1 pbTAL f GTATTGTCGCGGGTTCGTTC chr1: 47470631- 129 pbTAL r CTCAACCAATCCCCACTCCC 47470738 mbTAL f GTTTTAGGTTTCGTTAGTATGGG chr1: 47470570- 129 +mbTAL r CAAATTAAAATAAATCATTTAACCCATAA 47470698 mbTAL r TTAAAATAAATCATTTAACCCATAA DMRTA2 pbDMRTA f CGAAGATTTCGTAGGCGGGT chr1: 50659325- 145 +pbDMRTA r ACGACGCAAATAACGCTACGCA 50659469 pbDMRTA r GACGCAAATAACGCTACGCA mbDMRTA f TGTTTTAGAAGCGGGAGAAAG mbDMRTA r AAATAAAACCCCCGTATCCAAT +mbDMRTA f AATGTTTTAGAAGCGGGAGAAAG chr1: 50659041- 113 +mbDMRTA r AAAAATAAAACCCCCGTATCCAAT 50659153 DMRTAexp Af GCGGCGGTTAGCGTTAGTTTTTCGGTAG chr1: 50659366- 124 DMRTAexp Ar CGAAACGCCAACGTATCATAACGACGCA 50659489 PDE4B pbPDE f ACGTTTTAGGGACGGCGAAT chr1: 66030622-  77 pbPDE r AATCCCAACGACCGTCTACC 66030698 mbPDE f TTTCGTTTTGTATTTATGGTAGATGT chr1: 66030580- 115 mbPDE r CCAACGACCGTCTACCACTA 66030694 BARHL2 pbBARHL f CGTGGTATGGATTTCGGGGT chr1: 90967266- 111 pbBARHL r ACTCCTAACCCTAAACGCGA 90967376 mbBARHL f GTTTTTTTCGGTTTTTGTTCGA mbBARHL r TTTCTCCCAATTCCAATATCCA +mbBARHL f TGGTTTTTTTCGGTTTTTGTTCGA chr1: 90967815-  86 +mbBARHL r ACTTTCTCCCAATTCCAATATCCA 90967900 TBX15 pbTBX f GCGATCGGCGATTGGTTTTT chr1: 119331668- 100 pbTBX r GCGACGACACACGACCTAAA 119331767 mbTBX f TGAGGTTTTAGGTCGTGTGT +mbTBX f GGTGAGGTTTTAGGTCGTGTGT chr1: 119331740- 142 mbTBX r AAAACCTTAATCGACTCAAATAAAA 119331881 RUSC1, pbRUSC f GGGTGTAGTTGCGTAGCGTA chr1: 153557280- 142 C1orf104 pbRUSC r CCGAACCCTCCTCACCAAAA 153557421 mbRUSC f TAGTTGCGTAGCGTAGGGTA chr1: 153557285- 126 mbRUSC r TCACCAAAATCCTCCTAAAAC 153557410 GNG4 B pbGNG f ACGTAGTGTTGGTAAGATTTGTAGA chr1: 233880823- 149 pbGNG r ACAAAAACCGCTTATAAACGACGA 233880971 mbGNG f GTAGGTTTTTGCGTTGGAGATT chr1: 233880677- 141 mbGNG r ATTTTCGTTACTTCTCTATTCCCAAA 233880817 POU3F3 pbPOU3F f GGGGTTTCGCGTTTTGAGTT chr2: 104836866-  79 pbP0U3F r AACACCAAAACCCCCGCTAA 104836944 mbP0U3F f AAAAGTAATTAATCGGAACGGT chr2: 104836837- 134 mbPOU3F r ACACTTTCCCAAATACAAAAAAA 104836970 BOLL B/C pbBOLL f TTTCGAGTCGGGGCGTTTTA chr2: 198359264- 138 pbBOLL r TACCTAACCGCTCGCTCTCT 198359401 mbBOLL f GTTCGGTTTTGGGATTTTT mbBOLL r AATCCCAAAAACCGACTCT +mbBOLL f GAGGGTTCGGTTTTGGGATTTTT chr2: 198359331- 131 +mbBOLL r ACCAATCCCAAAAACCGACTCT 198359461 TRIM71 pbTRIM f CGGAGGAATTTGTGTCGTCG chr3: 32834331- 110 pbTRIM r CACCAAAACAACGCTACCCG 32834440 mbTRIM Af TTGGGAATTTTTTTCGTTTAT chr3: 32834188- 150 mbTRIM Ar TCCTCCGAATAACTTAAAAACC 32834337 mbTRIM Bf TCGTTGGATAGTGGTATTTAATGT chr3: 32834348- 150 mbTRIM Br AAAATCACCGACTCACTCAA 32834497 SLC2A2 pbSLC f CGGAGTACGGCGGTAGGAA chr3: 172228914-  80 +pbSLC r AATACCCCGAAAACCCGCTAATA 172228993 pbSLC r ACCCCGAAAACCCGCTAATA mbSLC f ATGATATTTTGTAGGAAAGCGT chr3: 172228748- 103 mbSLC r CAAATTCCGTTTCTAAAAAAAC 172228850 CYTL1 pbCYTL f GGGTTCGTATGCGGGAGTAG chr4: 5071974- 126 pbCYTL r ACGAAACTACACCAACGCCT 5072099 mbCYTL f GGGGGTTTTCGTTAGGAGTAG chr4: 5072020- 123 mbCYTL r AAACCGCCCTAAACCACC 5072142 SHISA3 pbSHISA f GAAGGGCGGTAGCGATAGTT chr4: 42094543- 108 +pbSHISA r CTACGAATTCCGCAAACCGAAA 42094650 pbSHISA r ACGAATTCCGCAAACCGAAA mbSHISA f ATTGTTTTTGTCGGCGTT chr4: 42094569-  86 mbSHISA r TACACTACGAATTCCGCAA 42094654 GABRA4 pbGAB f GCGTGCGTATATTCGCGTTT +pbGAB f CGGCGTGCGTATATTCGCGTTT chr4: 46690291-  95 pbGAB r AAATTCCGCCTCCCCTAACC 46690385 mbGAB Af TTTAGCGTTTAATGTGTATGTAGA chr4: 46690411- 135 +mbGAB Ar CGAAATTACAATCGAAACAAACTTAC 46690545 mbGAB Ar AAATTACAATCGAAACAAACTTAC mbGAB Bf GTTTTGAGTAGGGTGCGAG mbGAB Br AAAAAAACAAATTCCGCCT +mbGAB Bf GATGTTTTGAGTAGGGTGCGAG chr4: 46690248- 151 +mbGAB Br AAACGAAAAAAACAAATTCCGCCT 46690398 EGFLAM pbEGF f TGGTAGCGTTGTAAGGTGGG chr5: 38293231- 129 pbEGF r AAAAACAAACGCGACCCTCG 38293359 mbEGF f TCGAGTTTTGGTAGCGTTGTAA chr5: 38293223-  84 +mbEGF r AATACCCCGCAAAAAAAATCTACA 38293306 mbEGF r CCCCGCAAAAAAAATCTACA C5orf39 pbC5orf f ACGAGAAATTGGCGCGTTGA chr5: 43076304- 101 pbC5orf r AACAACACCCTTTACGACGC 43076404 mbC5orf f TGTTTGTTAGGGTTTTGTTTTAA mbC5orf r CGCCAAAACGAATATTTATTTA +mbC5orf f AATTGTTTGTTAGGGTTTTGTTTTAA chr5: 43076267- 124 +mbC5orf r CGACGCCAAAACGAATATTTATTTA 43076390 CDO1 B pbCDO f GGTAGCGTAGTGGATTCGGG chr5: 115180192- 142 pbCDO r CTCGTCCTCCCTCCGAAAAC 115180333 mbCDO f GTTTGTTTTATTTCGTGGGGAG chr5: 115179983-  85 mbCDO r CCAACTCCTTAACTCGCTCAA 115180067 IRF4 B/C pbIRF f TCGCGGGAAACGGTTTTAGT pbIRF r GCCCTTAACGACCCTCCG +pbIRF f TTTTCGCGGGAAACGGTTTTAGT chr6: 336451- 100 +pbIRF r GCGCCCTTAACGACCCTCCG 336550 mbIRF f CGTTTTGTAAAGCGAAGTTT +mbIRF f GTTATACGTTTTGTAAAGCGAAGTTT chr6: 336298- 108 mbIRF r AAACCAATCAATCACTAAACTACA 336405 ID4 B pbID Af GGTTTTTGGGCGTCGTGTTA chr6: 19945064- 107 pbID Ar AAATTCACTCTCCACCGCCC 19945170 pbID Bf AGGCGAATAATGAAACGGAGGA chr6: 19944950- 134 pbID Br TAACACGACGCCCAAAAACC 19945083 mbID f ATTTTACGGATGGAGTGATG +mbID f GGAATTTTACGGATGGAGTGATG chr6: 19945031- 118 mbID r CTTATCCCGACTAAACTACTAAAAAA 19945148 SCAND3, pbSCAND f AATTCGTTTCGCGACGTGAG GPX5 +pbSCAND f TTAATTCGTTTCGCGACGTGAG chr6: 28618249- 111 pbSCAND r ACACGCCTTAAAACCTACTCAT 28618359 mbSCAND f CGTGAGGGAGAATTTAGGAG chr6: 28618265- 104 mbSCAND r TAAAAAAACACACGCCTTAAAACCTA 28618368 DDAH2 pbDDAH f TCGTTTAGCGAGCGTTGTTT chr6: 31806112-  99 pbDDAH r GATCCGCCGTTACGCTATTC 31806210 mbDDAH f TGTTAGAAATCGGTATCGTTTA mbDDAH r TCTACGAAACGTTTACAACC +mbDDAH f TTTTTTGTTAGAAATCGGTATCGTTTA chr6: 31806097-  97 +mbDDAH r AAAATCTACGAAACGTTTACAACC 31806189 COL11A2 pbCOL f TTTAGGGATCGCGTTCGGAG chr6: 33269259- 144 pbCOL r AAACTCCTTTCCCCTCTCATAC 33269402 mbCOL f CGGAGTTTTTAATCGGATAT chr6: 33269274- 142 mbCOL r TCCCTTCTCTTTAAAACTCCT 33269415 NT5E B mbNT5E f GTCGGATTTTATTTTAATCGTG mbNT5E r AAACAAAAAAATCTCAAAAACTAAAA +mbNT5E f GTTGTCGGATTTTATTTTAATCGTG chr6: 86215769- 144 +mbNT5E r CTTAAACAAAAAAATCTCAAAAACTAAAA 86215912 SIM1 B pbSIM Af GTTAGGGGCGAGGCGTTTAT chr6: 101019614-  82 pbSIM Ar CGAAACCTAAACGCGCGAAA 101019695 pbSIM Bf AGGTTAATAGGTGGCGCGTT chr6: 101019077-  95 pbSIM Br CCCGCAACTCCGCGATAATA 101019171 pbSIM Cf AGTCGTTTTTCGCGCGTTTA +pbSIM Cf CGAGTCGTTTTTCGCGCGTTTA chr6: 101019667-  90 pbSIM Cr GACCCGACACCCTAAACTCAT 101019756 mbSIM Af AGGCGTTTATTGGTTAATAGGG chr6: 101019624- 134 +mbSIM Ar CGACCCGACACCCTAAACTCAT 101019757 mbSIM Ar ACCCGACACCCTAAACTCAT mbSIM Bf TTTAATTTGGGTTTTAAGTTTGAGG chr6: 101018944- 132 mbSIM Br ACGCTACTAAACCCCGCTTAT 101019075 RGS17 RGS17 Af GCGTTTAGGTAGCGACGC chr6: 153493700- 121 RGS17 Ar ATACCCCGACGAAAACGAC 153493820 RGS17 Bf TTTGGGATTTGGTCGAGC chr6: 153493620- 111 RGS17 Br AAAATTAAATCCCGCGTCG 153493730 CAPDS2 CAPDS Af CGTTTAGGTTTGTGGACGC chr7: 121743823- 129 CAPDS Ar AAAAACGAAATCGCTAATACGC 121743951 MSC MSC Af TTTTTCGAATTTTTGCGC MSC Ar AACACGCTCCGACTAACTTC +MSC Af GGTTGTTTTTTCGAATTTTTGCGC chr8: 72918397- 135 +MSC Ar TAAACACGCTCCGACTAACTTC 72918531 MSC Bf CGTTCGCGTTATTATTTGC MSC Br CGCCCAATAACAACTCGT +MSC Bf ATTATCGTTCGCGTTATTATTTGC chr8: 72918698- 155 +MSC Br CCTCGCCCAATAACAACTCGT 72918852 SPAG6 SPAG6 Af GTCGAGTCGTCGTTACGATC chr10: 22674453-  77 SPAG6 Ar CTACCCTCCTCGAACTCTACG 22674529 INA INA Af GTTTTCGGATGGGAAATTTTAG INA Ar AAACCATCTACATCGAAATCGC +INA Af GTGGTTTTCGGATGGGAAATTTTAG chr10: 105026593- 123 +INA Ar AACAAAACCATCTACATCGAAATCGC 105026715 FLI FLI Af TTTTTAGGAGTAAGTATTTTGTGTG chr11: 128068870- 112 FLI Ar CCCTCTTCCTCCCCTACTAAT 128068981 ATP5G2 ATP5G2 Af TAGGTATATTTCGGTCGGC chr12: 52357363- 116 ATP5G2 Ar AACTCGAAACCTCATCCG 52357478 USP44 USP44 Af ACGGGAGGGTAAATTTAGC chr12: 94466977- 114 USP44 Ar TACCAAACAATTCGACGTTA 94467090 POU4F1 POU4F1 Af GCGTACGTCGGTTTATTC POU4F1 Ar ACGCTCTACGCGATCAAA +POU4F1 Af AAGTGCGTACGTCGGTTTATTC chr13: 78075512- 141 +POU4F1 Ar GCGACGCTCTACGCGATCAAA 78075652 LHX1 LHX Af CGAGCGATTGTGGGGTTAGA chr17: 32368543-  82 LHX Ar CAACTCGCGACCGCCTAAA 32368624 HINF1B HINF Af TTCGGGCGTTTATAGAGTTC chr17: 33176898- 120 HINF Ar AAAATCAAAACGCGAACG 33177017 HINF Bf TAGCGTCGCGTTAGAAAGC HINF Br ATCGCTCAAAACCTAACGAA +HINF Bf TTTTAGCGTCGCGTTAGAAAGC chr17: 33177225- 117 +HINF Br AAAAATCGCTCAAAACCTAACGAA 33177341 HINF Cf AGGTTTAGTTTCGAAATCGC HINF Cr AACCGAACGATTCCCTAA +HINF Cf GTTAAGGTTTAGTTTCGAAATCGC chr17: 33177654- 120 +HINF Cr CTAAAAAACCGAACGATTCCCTAA 33177773 GALR1 GALR1 Af GAATTTTTGGAAAAGTCGGGA GALR1 Ar CTCCTACAAAAAAAACTCCC +GALR1 Af TTCGGAATTTTTGGAAAAGTCGGGA chr18: 73090886- 104 +GALR1 Ar CGACTCCTACAAAAAAAACTCCC 73090989 MAST1 MAST1 Af AGAAGGTGGTCGGTAAGC MAST1 Ar ACGTAATTATAAAAAACACGCC +MAST1 Af GGAGAAGGTGGTCGGTAAGC chr19: 12839386- 148 +MAST1 Ar AAAACGTAATTATAAAAAACACGCC 12839533 MAST1 Bf TAGTTTTTTGGAGGGAGAGG chr19: 12839568- 103 MAST1 Br ATCCTCGTCCTCTTAAAAAAC 12839670 CPXM1 CPXM1 Af GTCGAGTTTGGGATTTTGGT CPXM1 Ar AAACTCCTACTCGCCCTAACC +CPXM1 Af GGGGTCGAGTTTGGGATTTTGGT chr20: 2729097- 118 +CPXM1 Ar AAAAACTCCTACTCGCCCTAACC 2729214 NEURL2 NEURL2 Af TCGAGTTGGATAAGGCGTAC chr20: 43952304- 142 NEURL2 Ar CCGATAACACGACCGACATA 43952445 NEURL2 Bf TGTATGTCGGTCGTGTTATC chr20: 43952424-  82 NEURL2 Br TAAACGTACTACCTCCGACC 43952505 ACVRL1 ACVRL1f GGATGTGGGAGGTTCGGTTCGGGTG chr12:50587308- 136 ACVRL1r CCGCTCGCCCCTCGCTAAAACTACA 50587443 AFF3 AFF3f GGCGCGAGGTAGTTTTAGTACGTAGTTTTT chr2: 99542180-  78 AFF3r ATAACAACGTCGTCCTTTCCGCAAAACG 99542257 AKR1B1 AKR1B1f GGGGATTTTGTAAGTTCGCGCGTGGTTT chr7: 133794143- 108 AKR1B1r ACACTCTCCGCGCGACCTATATTAACGA 133794250 AKR1B1R_f GGAGACGGTTTGTTATGGTTGTTGCGTT chr15: 43266838- 122 AKR1B1R_r ACGCCCTTTCTACCGACCTCACGAACTA 43266959 ALDOC ALDOCf TTTTTCGGGGGCGTGGTTTGTATGTTT chr17: 23928071- 123 ALDOCr TACCTAACGAAACGCTCACTCCACCTCG 23928193 ALOX5 ALOX5f TTTTGCGGTTAGGTGAAGGCGTAGAGGT chr10: 45234654- 106 ALOX5r GACCGAATACCCCGCTTTCTCTCTCGAC 45234759 ALOX5R_f GAGGTCGAGAGAGAAAGCGGGGTATTCG chr10: 45234729- 110 ALOX5R_r AACGCTCTCAACCCAACCCCTAAACTCA 45234838 ALX1 ALX1f AGGATAGTAGCGGTGAGTCGTTAGCGTT chr12: 84198385- 117 ALX1r CGCTCCCACTTTTCTCCTTTCTCCCTCC 84198501 ALX4 ALX4f TTTTGATAAAGTGGGGAGGGCGTAGGGG chr11: 44289270- 106 ALX4r ACACTCTCAAATACCCGTCGCGCTCTAT 44289375 C1orf230 C1orf230f TTTTGATAAAGTGGGGAGGGCGTAGGGG chr1: 149960830-  92 C1orf230r ACACTCTCAAATACCCGTCGCGCTCTAT 149960921 C1orf230R_f AGCGTAGCGTAGTTGGAGTAGTTGCGAA chr1: 149960685- 121 C1orf230R_r CGACGACTCTCTTCCCAATCTAAAACCCCA 149960805 C6orf186 C6orf186f CGGAGTTTAGAAGGGCGTTCGGTTACGG chr6: 110785585- 116 C6orf186r CTCCACGAATCGCATCTTTCAATACCCA 110785700 C17orf64 C17orf64f AAAGGTGGTTCGAGTGAGGAAATTGCGG chr17: 55853711-  79 C17orf64r GCGTCCCTAAACGACACACGACGAAATC 55853789 C17orf64R_f GTCGACGGCGGTTTTATCGTATTGTCGC chr17: 55853578- 112 C17orf64R_r CCTTCTCCCGAACCTTCCTTCGTATCCT 55853689 C19orf41 C19orf41f TTAGAGGTATGGCGGGGTTTTTGTGACG chr19: 55358254-  95 C19orf41r AATACTCCCTAAACCTCCTAACCGCGCC 55358348 CCDC67 CCDC67f GAGGTTTAATTGTTTCGTTGGTCGC chr11: 92703424- 123 CCDC67r ACGCAAAACCGCGTATATCACCT 92703546 CCDC8 CCDC8f GGTTTTAGGGACGCGGTTGGAATTTGGG chr19: 51608460-  89 CCDC8r CCCAACGCCTCGACCATATTAAATAACTT 51608548 CD38 CD38f GCGATTAAGGCGTATCGGTGGGTATTGC chr4: 15389377- 125 CD38r AACACCACCCGACGAACTCTCGACTAAC 15389501 CD8A CD8Af TAGGACGTTGTTTGGTTCGAAGTTCGGG chr2: 86871471-  99 CD8Ar CTCCGAACCGACCGAAAAACGCAACTTT 86871569 CDH23 CDH23f GGCGGGGTATTGTTTTGTTTC chr10: 72826313- 111 CDH23r TCTACCGATATCATAACACCGACT 72826423 CDK5R2 CDK5R2f AAAGGTAGAGGGAAGGAGAGTTGTTTTT chr2: 219532251- 104 CDK5R2r ACTCCTACCTCCTCCGAATCCTAAAACCT 219532354 CHST2 CHST2f CGGAATGAAGGTGTTTCGTAGGAAGGCG chr3: 144322486- 151 CHST2r GCTACGACACCCAACGACCCATCGAAA 144322636 CLCN1 CLCN1f AATGATTTTGTTGGGTTCGGTGGAGCGG chr7: 142752740- 113 CLCN1r CCGACAACTTCCGCGCCATCTCTTAAAC 142752852 CLCN1R_f TTGTGTTTTGAGCGTAGGTTGCGCGTAG chr7: 142752798-  77 CLCN1R_r GCCTTCCCGTCGTAAAACAACTCCGACA 142752874 COL16Af COL16A1f GTTTTAGGGGGTTGGGGGTTTGTTAGGGA chr1: 31942237- 146 COL16A1r AACCCGAAACGAAACTATACACCCCGCA 31942382 CPNE8 CPNE8f TCGATGTTCGTAGTGTTGTTGTAGCGGT chr12: 37585569- 121 CPNE8r CCATCCCCGCCTAACGAAAACTAACCCT 37585689 DIO3 DIO3f CGTTTCGAGAAGAAGTTTCGCGGTTGGT chr14: 101095917-  89 DIO3r ATCTAAACCCAAATCGAAAACCGCCGCC 101096005 DNM3 DNM3f TTGGAGTTGTCGTAGATCGTCGTGGTGG chr1: 170077504- 123 DNM3r AAATCGCCCCACTACCGCATCCTTACTC 170077626 DNM3R_f GCGGTTAGGTGTGGTAAAGTAGTTGGCG chr1: 170077283- 123 DNM3R_r GCGCACAACCAACCTATAAACTCCGACG 170077405 DUOX1 DUOX1f GGGATTTGTGAAGGCGGATTTG chr15: 43209229-  79 DUOX1r AATATTCCGTCGATACCGAAAACCCGA 43209307 EMX1 EMX1f CGGTTGGAGCGCGTTTTCGAGAAGAAT chr2: 73005041- 123 EMX1r AACGCAAAACAAACCGCGACCGAAAATA 73005163 EMX2OS EMX2OSf AGGAGAAGTCGTAGCGGGCGTC chr10: 119291932- 101 EMX2OSr GACTAAACCTTCTACCGCCCACCG 119292032 ESPN ESPNf TAGTTGCGATGGGGTGGGAAGTTACGTT chr1: 6430246- 112 ESPNr AAAACCATCGCCATCCACGAAAACGACA 6430357 EVX1 EVX1f AGGAGGATGATAGTTTAGAAAGAAGAGGGT chr7: 27248900- 120 EVX1r CGCGACCGCGACGATAACGATAAAAACT 27249019 FABP5 FABP5f GAAACGTGTAGGCGTCGGCGTTTATGAG chr8: 82355078-  80 FABP5r CGACCTCTCGAACGCCTCCTACAAACAA 82355157 FBRSL1 FBRSL1f GTGGAGGAGGAAGTTCGTTTC chr12: 131575948- 105 FBRSL1r AACTACTACCAAACACGAAACGCA 131576052 FLI41350 FLIf GGTTAGAGTCGGTTGCGTAGTTT chr10: 102979731- 125 FLIr TTTTTGTTAGGCGAAGTATAGAGAGCG 102979855 FOXG1 FOXG1f TTTTTCGATTGGTCGACGGCGAGAGAG chr14: 28305617- 124 FOXG1r TTTCCGAACTACAAACGCACACTAAAAC 28305740 FOXL2 FOXL2f GATTCGTATGGGTTTTATCGAGTTTC chr3: 140148670-  95 FOXL2r ACTTAAAAATAAACTCGCCCGTACG 140148764 FZD2 FZD2f TCGTTGGTGAAGGTGTAGTGTTCGTTCG chr17: 39990814- 125 FZD2r TAACGCGCGCGCTCACAAATAAAACGAC 39990938 FZD2R_f TTTTTAGTGGTTCGAGCGTTTGCGTTGC chr17: 39990969-  91 FZD2R_r TCCGTCCTCGAAATAATTCTAACCGACGC 39991059 HIF3A HIF3Af CGTGGTATAGTTAATCGCGCGGCGT chr19: 51492066- 125 HIF3Ar TACAACCCCAACGCCATAACTCGCCAAT 51492190 HIVEP3 HIVEP3f TGTCGTCGTCGTCGGGGTTTTGTTATTT chr1: 41901039-  76 HIVEP3r ACGACGATAAACTCCCGCTAAACCCGAA 41901114 HIVEP3R_f GAACGAGGATTTGCGTTTTTGGATCGC chr1: 41901096-  80 HIVEP3R_r CCTAAACTCCTCTACATATTCCTCTACCT 41901175 HLA-F HLA-Ff GAATGGTTGCGATATGGGGTTCGACGG chr6: 946778- 125 HLA-Fr CCACGATATCCGCCGCGATCCAAAAAC 946902 HOTAIR HOTAIRf TAAGGGTCGGTTGTTGTTTTTTTTC chr12: 52645919- 116 HOTAIRr ACCGACGCCTTCCTTATAAAATACG 52646034 HOXA10 HOXA10f TGTGGGATAATTTGGCGAAGGGAGTAGA chr7: 27180403- 124 HOXA10r AACTCGAAATTAACTACGAACGCCCGCC 27180526 HOXD11 HOXD11f GGCGGGGGTAGTTTTTGTATTAAGGCGA chr2: 176680987- 125 HOXD11r CCTACGCTACTACTCTTCTCGACCCCCG 176681111 HOXD8 HOXD8f CGTTTCGTTCGTCGGTCGTAGCGATTG chr2: 176702636- 114 HOXD8r CCGACGAAACATTTTCGCACCACAACAC 176702749 HOXD8R_f CGCGGTTTCGGGGTATACGGAGTTTTTG chr2: 176702549- 120 HOXD8R_r GCAATTCAATCGCTACGACCGACGAACG 176702668 HSPA12B HSPA12Bf CGTCGTAGCGGGTACGGTTAACGAGTTG chr20: 3661361- 125 HSPA12Br TTTCTCCACTCGAAACGCCCGACAACC 3661485 ISL1 ISL1f CGGGGGAGAACGGTTTGAGTTTCGAGTA chr5: 50714776- 110 ISL1r TCATATTTCAACCTCGCCGCCGCTAAAC 50714885 Intergenic1 Int1f AGTAGGGATGGTCGTTCGTTGTTCGGTG chr11: 68379573- 107 Int1r GACAAACGACCGAAAATACTCGCGCAAC 68379679 Int1R_f TTTTACGGTCGGGGCGATAGTTGAAGGT chr11: 68379395-  99 Int1R_r TCACGCCAATACCCGCTAATCCCTCCTA 68379493 Intergenic2 Int2f GGGGATGGATAATTTTTAGGCGTTAAC chr17: 69460223- 117 Int2r TAACCTCGTCTTTATCCCCGCG 69460339 Intergenic3 Int3f AGTGTGTAGTCGTTTGTGGGTGAGGAGTT chr8: 95315865- 130 Int3r CACCGCGAAAAACGCCCACAATCTTACC 95315994 Int3R_f CGCGGGGGAGTTTATTTTTGAGGATTCGG chr8: 95315775- 118 Int3R_r ACTCCTCACCCACAAACGACTACACACT 95315892 Intergenic4 Int4f TAGTATTTGTACGGAGTTTTTCGGCGGTC chr5: 43054172-  92 Int4r TACGACGCAACCAACGATACTATCACCAA 43054263 Intergenic5 Int5f TAGTGATTGGTTATTTGGGCGCGGGGC chr10: 43138416- 115 Int5r AAACGACATCCATCATCTCCCTCGACCC 43138530 Intergenic6 Int6f AGGTCGCGTTTTGGTCGTGC chr3: 14827613-  76 Int6r ACTTAAAAATAAACTCGCCCGTACG 14827688 Intergenic7 Int7f ATTTTACGTAGGGTGGGGTTGAGGGCGT chr12: 52897799- 112 Int7r ATCCTAACCGTCCCGCCTCAAAACCGTA 52897910 Intergenic8 Int8f CGTCGTAGTATTTGGCGGCGCGTTTC chr2: 236737778- 106 Int8r AACGTACCTAATCCCCAAACCCACTCCT 236737883 Intergenic9 Int9f TCGTTGTGCGCGTTTCGTTTGTTGGATTA chr6: 778755-  92 Int9r TCGATAATATCTCCGTCGCCTCCGCAAA 778846 Intergenic10 Int10f GCGCGTTTAATCGTGGGATTTTTGGGAG chr2: 174899379- 116 Int10r CAAATTCGCGACACCCTACCCCAACAC 174899494 Int10R_f GGGTGTCGCGAATTTGGGGTA chr2: 174899479- 124 Int10R_r CTAAACCTCTCCCCTCCCAAATTTACCT 174899602 Intergenic12 Int12f ATCGAGTTTTTAGCGGTTTTTGGGGCGG chr1: 119344866- 109 Int12r ACTAACATCGCGCACTTAAATCTTTCCG 119344974 Intergenic13 Int13f GGTAGCGGCGGGTAAAAAGTC chr7: 64675119- 107 Int13r TACAACTTTTTACCTCCGCCGC 64675225 Intergenic14 Int14f CGTCGATTTGCGGAATTTCGTCGTCGTT chr1: 238227938- 108 Int14r ACATCCGCGTAAACTCGCCCTTTAACAC 238228045 Int14R_f TTTCGGGATTAGGGTTTCGGAGGGTGTC chr1: 238227822-  92 Int14R_r CGTATCGATCCGTCCCTCCCGCTTAAAA 238227913 Intergenic15 Int15f CGGTTTTGGTGGTAGTTTTGGTAATC chr19: 48895723-  80 Int15r AAAACCTCCCGAACGACGAAATAATCCA 48895802 Int15R_f GTAGGCGGTCGGAACGTGAAC chr19: 48895536- 125 Int15R_r CGATAAAAACTACAATAACTCGACAACCA 48895660 Intergenic16 Int16f GTTGTGAGGGTTTTCGGCGGTATC chr1: 54713046- 120 Int16r CATAACAACGCGCGACCCCTA 54713165 Intergenic17 Int17f TGATTATAAATTAGGGGGTTTGGTCGTCG chr12: 61311832- 114 Int17r AAACCCTCCACCCTCGCAATACTACTCC 61311945 Intergenic18 Int18f TGTAGGAGATAATGGGAGTGAAGAGGGA chr6: 4971256-  83 Int18r TTCCACGAAACGCGCGACTTCCTAACTA 4971338 Int18R_f GTTGAGTTAGGAGAGGTCGATAGC chr6: 4971467- 104 Int18R_r CCCGAAAACAACGACTATCGAAATCCAA 4971570 Intergenic19 Int19f ATAAGGTTTGGTGGAAGCGTAGGAGCGT chr6: 3177175- 115 Int19r ACGCCGAATAAAAATCCCGCAACCACAA 3177289 Intergenic20 Int20f GGAGGGGAGGAGATAGCGTTATTTAGGG chr10: 118912740- 103 Int20r AAACAAAACCCGAAACCCCACCTACACC 118912842 Intergenic21 Int21f GCGTGGTAGTTGAGGATGTAGACGTGGT chr16: 45381613- 124 Int21r TCCGAACTACTTAAAAATCCCCGCCGCC 45381736 Intergenic22 Int22f TCGTTGGTTGTGATTTTTATGCGGGCGT chr8: 68037259-  99 Int22r ACCTCTCCGATAAACCAAATCCTCCGCC 68037357 Int22R_f CGGGTGAGGTTTGTGGTTAATTTCGCGT chr8: 68037556- 120 Int22R_r CTCAACCAAACTACAACGTTCCCGCCTC 68037675 Intergenic23 Int23f AATGGAGGCGTAGATTAACGAGCGGTGT chr5: 42987147- 108 Int23r ATCCTTAACAACCCCGCCGACTAACGTC 42987254 Int23R_f ACGGGTACGGAGAAACGTCGGATTTAGT chr5: 42987852-  95 Int23R_r TCCCCGCGACACTCTACCTATAACGTCC 42987946 KCNH8 KCNH8f CGTTTGGCGGGTATTGTTGTTC chr3: 19164879-  93 KCNH8r CCCGACGCAAACTCCCTCTC 19164971 KCNJ2 KCNJ2f GAAGTTGTTTTTTAGGGGTTTGCGC chr17: 65676355-  86 KCNJ2r ACTCAAATCTACCCTCGCTTCAACG 65676440 KCKN4 KCNK4f GCGCGGGGGTATTTTGGAGGGTTAGTTA chr11: 63816449- 101 KCNK4r TCCCTACTCGCCCGCTACGACTATAACA 63816549 KCNK17 KCNK17f CGGATTTTGTTTTCGGGAGTCGTTCGGG chr6: 39390031- 120 KCNK17r AACTAAACGCCTAACCCTTCCCTCCCAC 39390150 KIAA1751 KIAAf TTCGTTTTGTTTTTCGGTTGGAGCGGGT chr1: 1925171- 118 KIAAr TATAACCTAACCCTTCAACCGCGCCTCG 1925288 KIAA1751R_f AGGCGGCGGTTTTTGGCGATTGTTTTTC chr1: 1925065-  76 KIAA1751R_r TTCCGTTACCATAAAACTACCCGCCCC 1925140 LASS1 LASS1f GATTTCGCGTATCGTCGTGTC chr19: 18868171- 103 LASS1r TAATATCCCCCGTACCCCCCG 18868273 LOC255167 LOCf TTTCGATAATAGCGTTTTTGCGGCGTGG chr5: 6636474- 146 LOCr CAAAAACACGCGACCTACGCCCTCCTAA 6636619 LRRC4 LRRC4f CGAGTCGGAGTGAGCGTTAAGTGAGGGG chr7: 127459680- 101 LRRC4r CCTATCAACGACCACCCAACTACTCCCT 127459780 MIR155HG MIR155HGf TCGGGTTTAGCGTCGTTTGTAGTTTCGG chr21: 25856335-  96 MIR155HGr AAAAACGTCTCCTTAATTCCCCGCGCTT 25856430 NEXN NEXNf GCGGTTGGAGTAGAAGTGTTAGCGGTTAGA chr1: 78126913- 124 NEXNr TCACCCTACAAAAACCGATAACCGACGA 78127036 NKX2-1 NKX2-1f AGTTGGTTATAGGCGGCGAATTGGGTTT chr14: 36057307-  91 NKX2-1r TCAACACCCCCTCTCCTAACCTCTCCAA 36057397 NKX6-2 NXX6-2f CGGGGAAGAGTTTCGGTTCGCGTTTTAG chr10: 134449988- 123 NXX6-2r CCCTCCTATAACCCCGACCTACCCGAAA 134450110 NKX6-2R_f GCGCGGTAGGTGTTTTTCGGGTTGTAAA chr10: 1344419796-  97 NKX6-2R_r ACCTTTACCTAACTACACTCCCATCCAA 134449892 NOTUM NOTUMf AGAGTAGGTCGTGGGGGATTC chr17: 77512836-  87 NOTUMr CGCGCTAACCGCGATAAAAAC 77512922 NRN1 NRN1f AGGAGCGGGAGAGGGAAAAATAGTTAAG chr6: 5952635- 125 NRN1r ACTACGCCCAAAACTCAACTACTAAAT 5952759 PLTP PLTPf TGGGAACGGGATAGGGACGCGTTTTAAT chr20: 43974093-  92 PLTPr GAATCCCCTAAACTACCCGCCATCCCAC 43974184 PLTPR_f TGTACGCGTATTTTTGGAGGGTGGTTTGC chr20: 43973871-  80 PLTPR_r CGATCTAATCGACCACCTCCTCTCCTCC 43973950 PRDM13 PRDM13f AAGTTTCGTCGAGTTGGGGTCGTTGGTT chr6: 100168753-  92 PRDM13r GACCCTTCCCGACAACCATCTCGAACA 100168844 PRDM15 PRDM15f GAAAATTGCGCGGTTGGGTTAGTAGGGG chr21: 42110148- 112 PRDM15r ACCTACAAATACCGTCCCCACCCGAAAC 42110259 PTGDR TGDRf AAGAGGGGTGTGATTCGCGAGTTTAGAT chr14: 51804089- 110 TGDRr CCGCGCGCGACTCGAACGAAAAA 51804198 RECK RECKf AAGGGTGCGATGTTTTCGTTTAGGATCG chr9: 36027398-  88 RECKr TAACTAACTAAAACCGCGATAAAACGACT 36027485 RTN4RL1 RTN4f TGGTAATCGCGTAGGTGTGTGATAGGGC chr17: 1827825- 107 RTN4r AAAATACAAAATACGCCCCCGACCCCGA 1827931 RTN4RL1R_f TGAGGAGAGATTCGGAGTAGTTAGTAGA chr17: 1827743- 109 RTN4RL1R_r CCCTATCACACACCTACGCGATTACCAA 1827851 SFRP5 SFRP5f TTTCGAAAAGTTGGTAGTCGGCGGTTGG chr4: 154929548- 123 SFRP5r CATTCTACTCCCCCGAATCGAAACCCCC 154929670 SFRP5R_f AAGAGGAAGAGTTCGCGCGTCGAGTTTA chr4: 154929355- 100 SFRP5R_r GAAATCGCGCGCCCACGATACTACAAAA 154929454 SHF SHFf TTATTAGTAGGCGGCGTCGGGGGTT chr15: 43266978- 150 SHFr CGAAAACCCCTACTCCGAAAAATCGTCCG 43267127 SHFR_f GTTGAGATATCGAGGGGTTCGGGTTAGG chr15: 43266838- 122 SHFR_r CGCCAACAACGATAAAATAAATACCGCGCC 43266959 SHOX2 SHOX2f CGTTTGTTCGATCGGGGTCGTACGAGTAT chr3: 159304063- 100 SHOX2r TTTCCGCCTCCTACCTTCTAACCCGACT 159304162 SNCA SNCAf GGTTGGGGGAGTGGGAGGTAAATTCGTT chr4: 90977105- 117 SNCAr CTAAACGCTCCCTCACGCCTTACCTTCA 90977221 SNX32 SNX32f TTGAGGGAAACGCGGTGGGAATCGTTTT chr11: 65357939- 119 SNX32r CCGTAACTCGCCCGAAAAACTAACCGAA 65358057 SP9 SP9f TGATTGGTTGCGGGGTAGTTTC chr2: 174907826-  86 SP9r ACACCCGCTTTAAAATACCGCTAA 174907911 STK33 STK33f GCGTTTCGGGTCGTTCGTTTTATTTCGC chr11: 8572140- 123 STK33r CGACAACCTACGCCGAATATACGCACCT 8572262 SYNGR3 SYNGR3f GAAGGGATGAGGTTGAGGTTGGAGGTCG chr16: 1981075- 121 SYNGR3r ACCTCCTACCCACCAATTCCGAAAAACAA 1981195 T Tf TTACGGAGTTTTAGGCGGCGTTAC chr6: 166501979- 121 Tr CATTTCCCTCTCTACGCGCGAAC 166502099 THBS2 THBS2f CGTAGGTTTTGTTGGAGCGAGAGATCGG chr6: 169395805-  94 THBS2r ACATATAAAACCGCGCTACCCGAAAACCG 169395898 TLX1NB TLX1NBf TGAAAGGGGAGAGGGGAATGTTATTGTT chr10: 102871413- 106 TLX1NBr AATATTCTCGCAAACCCACCGCCAAACC 102871518 TMEM22 TMEM22f AAAGAGATTCGTGTTGCGGCGGATGAAG chr3: 138021575- 117 TMEM22r GATCAACACTCGAACCCGAACTTTCCGC 138021691 TNFRSF10D TNFRSf AAGGGAGGAGGGTGGATCGAAAGCGTTA chr8: 23077397-  79 TNIFRSr CGAAAACCTTTACACGCGCACAAACTACG 23077475 TXNRD1 TXNDR1f TATGGGTTGCGTCGAGGGTAAGGTAGTG chr12: 103133710-  79 TXNDR1r ACCATCGCCGTTCTTACCTTTCGTCTACA 103133788 VSTM2B VSTM2Bf TTTTTAATTCGGTTCGGCGTTGATTTGT chr19: 34711435- 125 VSTM2Br ACAACCGCGCGCTCCCGATAC 34711559 ZFPM2 ZFPM2f TAGCGCGGAAGTTGTGAGTTTAAGGCG chr8: 106401146-  96 ZFPM2r TCCTCTAAACACCATCGAAACCCCCGAAC 106401241 ZNF280B ZNF280Bf AGTGGCGTTCGTTGAGATTAGGGAAGGG chr22: 21192757- 121 ZNF280Br ACCGTACGCTACCGAAACGACCTTTACA 21192877 LOC105378683 LOC105 Af GTTTGTAATTGGTATGAGCGGC chr1: 43023566- 108 LOC105 Ar ATAACGAAACGACGCCTC 43023673 LOC105 Bf GTAATTGGTATGAGCGGCGT chr1: 43023570-  91 LOC105 Br GCCTCCGCGAAATAAAACCAT 43023660 LOC105 Cf AGTTAGAGTGGGTTAGGGGAT chr1: 43023464 150 LOC105 Cr ACGCGTAACACAAACACGAC 43023613 NPHS2 NPHS2 Af GGGGGATTTTAAAGATCGTC chr1: 177811721- 122 NPHS2 Ar GACGAACGCAATCCACAA 177811842 NPHS2 Bf TGGTGGAGTTGTGGATTGCG chr1: 177811817-  75 NPHS2 Br TCCCACCCAAACCTCTCTCT 177811891 NR5A2 NR5A2 Af GGTGCGTTTACGGGTTTC chr1: 198278389- 150 NR5A2 Ar ACCTAATCCGATATTTCCCGA 198278538 NR5A2 Bf GGTAGGGTTTCGGTTGCGTA chr1: 198278432- 139 +NR5A2 Br TATTTCCCGAAAACTCCACATCCA 198278527 NR5A2 Br TCCCGAAAACTCCACATCCA PAX6 PAX6 Af ATTTGGATGTTTCGCGTTTC PAX6 Ar TATCGCTACGACCCGACTAA +PAX6 Af GTTAATTTGGATGTTTCGCGTTTC chr11: 31783206- 117 +PAX6 Ar GTTTATCGCTACGACCCGACTAA 31783322 PAX6 Bf AGGGGAGTCGCGTTTTTAGG chr11: 31782520- 133 PAX6 Br TCCCGACCGAAACCCAAATC 31782652 KCNE3 KCNE3 Af GAATAACGGCGTAAGTTTTTAC chr11: 73855818-  98 KCNE3 Ar ATCCTCCCGAACGCAATA 73855915 KCNE3 Bf TTGTACGTTTGTGGGTGTGGA chr11: 73855765- 150 KCNE3 Br TCCTCCCGAACGCAATAATCG 73855914 KCNA6 KCNA6 Af TTAACGGTTAGGTTAGATCGC chr12: 4789322- 100 KCNA6 Ar CAATCTCTAAAACGCGACAC 4789421 KCNA6 Bf CGGGTGTCGCGTTTTAGAGAT chr12: 4789399-  84 KCNA6 Br TTCTCCGATCTCATACCCCCT 4789482 TMEM132C TMEM Af GAGAAAAGTTGTTTCGGTC TMEM Ar GCTACGTCTCTACTATCCGA +TMEM Af CGGGAGAAAAGTTGTTTCGGTC chr12: 127317663- 124 +TMEM Ar CCGCTACGTCTCTACTATCCGA 127317786 TMEM Bf TTCGGGGTGAGGGTAGTC TMEM Br CCGACGCCCAACTAAAAA +TMEM Bf GAGTTCGGGGTGAGGGTAGTC chr12: 127318043- 137 +TMEM Br GAATCCCGACGCCCAACTAAAAA 127318179 TMEM Cf TTTTCGGGTTACGGGTCGTT chr12: 127317330-  95 TMEM Cr ACGACTCCTCCGAAAATCCG 127317424 PDX1 PDX1 Af GTCGATTTTTGTTTTGAGC chr13: 27390195-  86 PDX1 Ar TAAAAATAATCTACCGAATCGC 27390280 PDX1 Bf GGCGTTAGCGGGGATTTAGA chr13: 27389563- 132 PDX1 Br CGCATCAAACGAAACCCTCC 27389694 PDX1exp Af CGGGAAGGTGTTCGTTTAATGGTTCGGT chr13: 27389489- 102 PDX1exp Ar GTTTCCGCTCTAAATCCCCGCTAACGCC 27389590 PDX1exp Bf GGAAAAAGGAGGAGGATAAGAAGCGCGG chr13: 27396588-  98 PDX1exp Br CTCGCCGAAAATCACGACGCAATCCTAC 27396685 EPSTI1 EPSTI1 Af TAGGGGAGGCGTCGAGTTC chr13: 42464253- 117 EPSTI1 Ar ACTCGCTAAACGTCCCAACC 42464369 A2BP1 A2BP1 Af GAGTTTAGGGGTCGCGTC chr16: 6009425- 140 A2BP1 Ar CAATACCGCCGCCTCTACTA 6009564 A2BP1 Bf GAGAGAGTAGGAGCGGATCG chr16: 6009706- 137 A2BP1 Br ACAAATCAACCCCGCCCTAA 6009842 CRYM CRYM Af AGTGAGTGTTCGGGAGTTTC CRYM Ar TCATTTATTAAAAACGCGCG +CRYM Af GCAGTGAGTGCTCGGGAGCCCC chr16: 21202786- 149 +CRYM Ar GGTTTTCATTTGTTAGAGGCGCGCG 21202934 CRYM Bf CGGGTTCGCGTAGGATTAGG chr16: 21202650-  83 CRYM Br ACTCCTCATCCCAACACCCT 21202732 PRKCB PRKCB Af GTTCGTAGTTCGCGGTTTC PRKCB Ar CGATACTCTCCTCGCCCT +PRKCB Af TCGGTTCGTAGTTCGCGGTTTC chr16: 23754928- 125 +PRKCB Ar GCACGATACTCTCCTCGCCCT 23755052 PRKCB Bf TTGGGCGAGTGATAGTTTC chr16: 23754821-  89 PRKCB Br GACCGCTACTACACCCGA 23754909 PRKCB Cf CGGTAGAAGAACGTGTATGAGGT chr16: 23755076- 141 PRKCB Cr GCTACCCTCGAAAACCCGAA 23755216 IRF8 IRF8 Af GATTTTTTTTAAGGTCGCGC chr16: 84490230- 112 +IRF8 Af TTACGATTTTTTTTAAGGTCGCGC 84490341 IRF8 Ar ACTATACCTACCTACCGCCGTC IRF8 Bf ATTTCGAAGAAGGCGGGTCG chr16: 84490149- 128 IRF8 Br CTCCAAACGATACGCCAACG 84490276 SALL3 SALL3 Af TTTTGCGGGTAAGCGTTC SALL3 Ar CCACAACTCTCTCGACGAC +SALL3 Af TGTTTTTTGCGGGTAAGCGTTC chr18: 74841456-  96 +SALL3 Ar GCCCACAACTCTCTCGACGAC 74841551 SALL3 Bf ATTTCGGGAAAGGGTGGGTC chr18: 74840051- 113 SALL3 Br ACCCTAATCCCCCTTCACCA 74840163 SALL3 Cf TTTCGTTTCGTTTCGGTCGC chr18: 74840452- 122 SALL3 Cr AACCCGCCCGAACTCAAATA 74840573 LYPD5 LYPD5 Af ATTAGGAGCGTACGTTTATTC chr19: 49016646- 143 LYPD5 Ar TACGCACTCGAAACACAA 49016788 LYPD5 Bf CGGCGCGTTTTAAGGGTTTT chr19: 49016738- 126 LYPD5 Br ATTACTCTCACCTCCGCACG 49016863 DPP10 DPP10 Af GATTGCGGGAAGAAGGTAC DPP10 Ar AAACGAAACCAAACGACAA +DPP10 Af CGGATTGCGGGAAGAAGGTAC chr2: 115635638- 102 +DPP10 Ar GACGAAACGAAACCAAACGACAA 115635739 DPP10 Bf TTTTCGAGTTTGAAGCGTTC DPP10 Br CGACTCTCACCTAATCCGC +DPP10 Bf CGGTTTTCGAGTTTGAAGCGTTC chr2: 115635947- 142 +DPP10 Br TACCGACTCTCACCTAATCCGC 115636088 DPP10 Cf TTACGACGGGGAGTTCGTTC chr2: 115635821- 123 +DPP10 Cr CTTAACAACGTTCGCAAATCACGA 115635943 DPP10 Cr ACAACGTTCGCAAATCACGA C20orf56 C20orf Af GTTCGTTATTTCGGAATTC chr20: 22507658- 147 C20orf Ar CCGACCGATAAAATATAATTC 22507804 C20orf Bf GGGAGGGATTTAAGCGGGAG chr20: 22507684- 136 C20orf Br CCCCCTTCACTAATCCCGAC 22507819 SOX2OT SOX2OT Af AGTGTTGAGAGTCGACGC chr3: 182919951-  92 SOX2OT Ar AATAAAATAACCCGAACCGC 182920042 SOX2OT Bf GGGTTACGGTTTCGGGTTGT chr3: 182919884-  86 SOX2OT Br CGCGTCGACTCTCAACACTA 182919969 CDKL2 CDKL2 Af GGTCGAGTCGAGTCGTTAC CDKL2 Ar AAAACGCCTCCTAACGAA +CDKL2 Af ATTGGTCGAGTCGAGTCGTTAC chr4: 76774785- 151 +CDKL2 Ar ACAAAAAAACGCCTCCTAACGAA 76774935 CDKL2 Bf TATTTTTGGGCGAAGGCGTTG chr4: 76774698- 109 CDKL2 Br GTAACGACTCGACTCGACCA 76774806 MARCH11 MARCH11 Af TCGGCGTTTTCGTTTTTC chr5: 16232623-  75 MARCH11 Ar CGACGACACAACCATAAACTTT 16232697 MARCH11 Bf AAGGTTTTGTAGTTGCGGCG chr5: 16232839-  97 MARCH11 Br TCTCACGCGCAACCGAAT 16232935 CCL28 CCL28 Af GTGGAGTTTTAGGTAGCGC CCL28 Ar ACCCGCGATAAACTAAACC +CCL28 Af AGGGTGGAGTTTTAGGTAGCGC chr5: 43433001- 128 +CCL28 Ar AACAACCCGCGATAAACTAAACC 43433128 CCL28 Bf TGTAGTCGTGGTTGTCGTGG chr5: 43432695- 140 CCL28 Br CCAAATAAACGACGTCCCGC 43432834 AP3B1 AP3B1 Af ATTTTATAGTCGCGTTAAAAGC chr5: 77304383- 137 AP3B1 Ar ACTTTTATTACTCGCGATCC 77304519 AP3B1 Bf GGTAGGGTGAGTTTGGTCGG chr5: 77304339- 146 AP3B1 Br CGCCGAACCACGTAAAAACT 77304484 CARD11 CARD11 Af ATTTGGGGCGTTTATGTTTC chr7: 3049825- 120 CARD11 Ar CCCTCGAAAAACGACTCC 3049944 CARD11 Bf AGGGGTTGTAGGGTCGGG +CARD11 Bf TTTAGGGGTTGTAGGGTCGGG chr7: 3049955- 133 CARD11 Br ATTTTACATTTCCCTCCCCCGC 3050087 BLACE BLACE Af AGAATAAAAGTAGGCGGC chr7: 154859246- 139 BLACE Ar TCTCGAAACCAAAATAAACG 154859384 BLACE Bf AGTAGGCGGCGGATTTGTAG chr7: 154859254- 104 BLACE Br CCGAAAATACGCGAAATCAACC 154859357 PTPRN2 PTPRN2 Af GAGGAGATAAAGGTGTCGC PTPRN2 Ar AACGTACCTAACCCGAAAAC +PTPRN2 Af TCGGAGGAGATAAAGGTGTCGC chr7: 157176188- 155 +PTPRN2 Ar CCAACGTACCTAACCCGAAAAC 157176342 PTPRN2 Bf GACGGTTTCGGTAGGGTC PTPRN2 Br CCGAACCGAATATAAAACGA +PTPRN2 Bf CGGACGGTTTCGGTAGGGTC chr7: 157176379-  85 +PTPRN2 Br GCGCCGAACCGAATATAAAACGA 157176463 RUNX1T1 RUNX1T1 Af TTAGGTTCGTAAAGAGGGC chr8: 93183286- 116 RUNX1T1 Ar TTAAAACCACGTCCGAATA 93183401 RUNX1T1 Bf TTTCGGGCGGGAGTTATAGG chr8: 93183412- 118 RUNX1T1 Br ACGCGCTCTAAACTCAACCG 93183529 L1TD1 L1TD1 Af GCGCGTGGGGYFCGTAGCGTTTTAAG chr1: 62433357- 109 L1TD1 Ar TTACCCGAAACACCCCGCGCCCTTC 62433465 PPFIA3 PPFIA3 Af AGATACGGAGATTTAGCGCGAGATCGGT chr19: 54337953- 143 PPFIA3 Ar AAATTAACCGCCGAACACTCACAATACG 54338094 FILIP1L FILIP1L Af TTGTAGTGTCGCGTTGCGAGTCGATTGT chr3: 101077651- 103 FILIP1L Ar ACAATAACGTAACGCCCATAAACCGAACG 101077753 NUDT16P NUDT Af GAGGACGGGTTGAATCGTGGTTTGTTGG chr3: 132563775-  84 NUDT Ar ACTACGATAATCAAAACGCTCCACGCGA 132563858 TOP2P1 TOP Af GTGCGCGTTTTAGTAGGGCGAGAATGG chr6: 28283268- 150 TOP Ar CGAAAACCAAATCCGAACCACCGTCTCC 28283417 TOP Bf TGATTTGGGTGGATGTAGAGGTTGTGGT chr6: 28283447- 122 TOP Br TTTCGAATAACGCTACTCCGAACCGCGA 28283568 UNKWN1 UNKWN1 Af TTGAGAGTAGGGATTGTGGTGCGTCGTC chr5: 72634694- 145 UNKWN1 Ar CTAACTCCCGAACGCTACATTCGCTCCA 72634838 GALR3 GALR3 Af GGTTGTGGTGAGTTTGGTTTACGGGCG chr22: 36550907- 143 GALR3 Ar CGTAAAACGCGACCACCGCCAACATA 36551049 PRSS27 PRSS Af GGGAGGTTATTCGTAGGATTTGGCGCGG chr16: 2705610- 139 PRSS Ar ATCCTAACGACTACGCACTACTTCCGCA 2705748 SLC7A4 SLC Af GAGTTCGTTTAGTTCGTCGGCGTC chr22: 19716858- 148 SLC Ar AACCCCGATAAACTCCGATAACGACCT 19717005 LEF1 LEF1 Af AGAGTTGGGGGCGGTATAGTTAGGGTGT chr4: 109307444- 104 LEF1 Ar TTCAATCCCTACGACCCCAACGCCTAAA 109307547 NFIC NFIC Af CGTGGATACGAGTTTTGGCGGCGATTAT chr19: 3386117- 103 NFIC Ar GCCACCAACCCTACCTCCTTCCATATCC 3386219 NFIC Bf TTTTTCGGTTTGAGTTATCGTGGCGGGA chr19: 3386234- 146 NFIC Br CGAACCGTACTTCCAACCAAACGCAACT 3386379 TMEM90B TMEM90 Af TAGGAAGGGGTCGATGTTGGTTTGGGTT chr20: 24398648- 100 TMEM90 Ar TCTCACCAACTCCCATCGAATTCGCACA 24398747 TMEM90 Bf GTTTTGGTTTCGTTTCGGAGCGCGTAGA chr20: 24398510- 133 TMEM90 Br TTTCTCTACCGACTCAACTCCCCCTCCC 24398642 UBD UBD Af TCGGTTGCGTAAATCGCGTTTTTGGTTG chr6: 29629437- 128 UBD Ar TTCTCGATAATATCTCCGTCGCCTCCGC 29629564 GIPC2 GIPC Af GTTTAGGGGTGGAGGTCGGGGTTTTGA chr1: 78284199-  91 GIPC Ar CCGAACCCCGCGCAAATAAAAACAACCT 78284289 EFNA4 ERNA Af GGGGCGCGTTTTTATGGAAAGTTAGGGT chr1: 153310423- 127 ERNA Ar CTACGCCCTAAAACACGCCTCGACTTCT 153310549 ERNA Bf TGTGCGAAAGAGACGCGGGGTTTAGTTA chr1: 153310139- 150 ERNA Br CCCGTAATCGCTAAAACATCCGCCCTTA 153310288 DRD4 DRD4 Af CGTCGGGCGATGTTGGTTTGTTCGTG chr11: 627035- 141 DRD4 Ar GCGACGCTCCACCGTAAACCCAATATTTA 627175 TCTEX1D1 TCTEX Af CGGGGAGGGTCGAGGGTTTTGTTTGAG chr1: 66990668- 101 TCTEX Ar GCGTCCCAAACTTCATTCAACCGACGAC 66990782 PHOX2B PHOX Af GCGGACGTAGTAATGGATTAAACGGGGA chr4: 41447111- 145 PHOX Ar AAATCCGACTCCCTACACTCCCGACTTT 41447255 TSPAN33 TSPAN Af GGGGGTTGTGTTAGTTGTTTGTTTAGCGA chr7: 128596487- 107 TSPAN Ar CGAAACTATTTCCCGCCAAACCGAACCC 128596593 CA9 CA9 Af TTTCGGGCGGGAGTATCGGGTTTTGTAG chr9: 35666101- 139 CA9 Ar GCTCCTTTACCCCTTCTCGACCAACTCC 35666239 UNKWN2 UNKWN2 Af TTACGGATTTTATTTGTATTCGGAATCGTA chr10: 102409232- 104 UNKWN2 Ar ACGCATCAAACTCGACACAAAATTTCATC 102409335 WT1 WT1 Af GGTGTTTTCGTAAGACGGGGTAGTGGGT chr11: 32406776-  94 WT1 Ar TTCTCCTCCGCTAAAAATCCGAATACGA 32406869 OTX2 OTX2 Af AGGGATTGTATTTCGAGGTGGTCGAGGT chr14: 56331673- 109 OTX2 Ar CCGACAAATCGAAACCTTCGCCCGAAAC 56331781 HOXB13 HOXB13 Af TCGCGGGTTATAAATATTTGGTTGCGGC chr17: 44157793-  93 HOXB13 Ar GACCGCCACTACCTCGAAAACATTTCCC 44157885 BRCA1 BRCA1 Af GGTAACGGAAAAGCGCGGGAATTATAGA chr17: 38530874-  95 BRCA1 Ar CCCACAACCTATCCCCCGTCCAAAAA 38530968 ITPRIPL1 ITPRIPL1f TTTTGTACGTTGGGTTACGGGGGTTTGG chr2: 96354715- 143 ITPRIPL1r TAAACGCGATAAACCCCTACGACCCCCA 96354857 HES5 HES5-F TATCGGTTTTCGTAGTTGCGGGAGGAGG Chr1: 2451323- 118 HES5-R CCGAATAAATACCAAACTCGCCCGACGC 2451386 CSRP1/ CSRP1/ CGGGTAGAGGGGAGGTAGGAATTGGAGA Chr1: 199775889-  80 LOC376693 LOC3766 199775914 CSRP1/ CCGAATAAACGTCACCCCTACACACCGC LOC3766 ALOX5 ALOX5-F TTTTGCGGTTAGGTGAAGGCGTAGAGGT Chr10: 45234681- 106 ALOX5-R GACCGAATACCCCGCTTTCTCTCTCGAC 45234732 PPM1H/ PPM1H/MON2- AGGAGTAGTATTGCGAGGGTGGAGGGT Chr12: 61311943- 112 MON2 PPM1H/MON2- TAAACCCGAAAAACAACGCCAATCCCGC 61312001 KIAA0984 KIAA0984-F GGGGATTTGTTGTAGAGTCGTAGGAGAA Chr12: 63515983-  62 KIAA0984-R CCGCATCCCACCCTTTAAAACTCTA 63516043 TXNRD1 TXNRD1-F TATGGGTTGCGTCGAGGGTAAGGTAGTG Chr12: 103133737-  86 TXNRD1-R TACGACGACCATCGCCGTTCTTACCTTT 103133768 CHST11 CHST11-F AAATTTGGATTGGGGGAGGGACGAGGTT Chr12: 103376469- 124 CHST11-R CTTCGCAACCGAACTACTCACCCCCGAC 103376538 EFS EFS-F GGTCGTTGGAGTGGTCGTTTCGGTTTAG Chr14: 22904743-  98 EFS-R CCTCAAACCCCCGAACGCGCTAAATAAA 22904785 ANXA2 ANXA2-F GTTCGGGGAGGGAGGGAGATTCGTTTTG Chr15: 58478046- 107 ANXA2-R AACTCCCGACTTTAACCTCCCAACCCAA 58478098 RHCG RHCG-F GTTGTAGGGGTGTTTGGTCGGGTTGGTA Chr15: 87840807- 118 RHCG-R ATCAACTACTCCGTACCCCACGTAACCG 87840869 RARA RARA-F AGTCGGGGTTGGTTGGTGGAAGAGG Chr17: 35718896- 137 RARA-R CCCTCTCAACTCGATTCAAAATTCCCCC 35718981 PTRF PTRF-F AAAGTAATAAGTGGTTTCGGGCGGAGTC Chr17: 37827277- 104 PTRF-R ACCCCGCATACCTACGAAAACGAAAACC 37827326 RND2 RND2-F CGGGATTATGGAGGGGTAGAGCGGTCG Chr17: 38430910-  99 RND2-R ACGTCCTTAACGAACACCTACAACAACG 38430955 TMP4 TMP4-F AGGTTTTGTAGTAGTAGGCGGACGAGGC Chr19: 16048446- 121 TMP4-R ACGAATACGAAACCCGAAACCGAAACGC 16048512 HIF3A HIF3A-F CGTGGTATAGTTAATCGCGCGGCGT Chr19: 51492259- 118 HIF3A-R TACAACCCCAACGCCATAACTCGCCAAT 51492376 KLK5 KLK4-F TAGCGGGGATTTATTAGGGGAGAGGTGG Chr19: 56107959- 123 KLK4-R ATCACCTACGAACACTATCCCTCACCCG 56108027 AMOTL2 AMOTL2-F GCGGAATAGTTCGCGGTTTTGGAATGTT Chr3: 135565786- 125 AMOTL2-R AAACGTTTCCGCTCCCCGAAAAACGAAT 135565856 SCGB3A1 SCGB3A1-F GGAGATAGTTTTGAGAGGGGGAGGTCGC Chr5: 179950858- 120 SCGB3A1-R CGCTACCTACGCCGATCGTAAATCCCAA 179950923 HLA-F HLA-F-F GAATGGTTGCGATATGGGGTTCGACGGA Chr6: 29799978- 112 HLA-F-R CGCGATCCAAAAACGCAAATCCTCGTTC 29800035 HLA-J-1 HLA-J,  GGTTTTGGTCGAGATTTGGGCGGGTGAG Chr6: 30082430- 101 NCRNA00  30082476 HLA-J, CCCGAATCCTACGCCCCAACCAAATAAA NCRNA00 HLA-J-3 HLA-J,  TGAGTGATTTCGGTTCGGGGCGTAGATT Chr6: 30083115- 125  NCRNA00 30083168 HLA-J, CGAAAATCTCTACAAATCCCGCAACCTCG NCRNA00 PON3 PON3-F ATGGTTTCGGGGTGTTTAGCGGCGATTG Chr7: 94863624- 105 PON3-R AACGAAACCGAACGAACCCCAATCCGTA 94863674 LRRC4/SND1 LRRC4-F GAGTCGGAGTGAGCGTTAAGTGAGGGG Chr7: 127459707-  77 LRRC4-R TCCCTCCGACCGACCCAAAATAACTACG 127459730 PAH PAH-F TTCGTTGTTCGTTTTGGGTAAAGGGAAG Chr12: 101835348- 116 PAH-R AAACTCGCTTCCCAAACTTCTAAAAATC 101835409 EPSTI1 EPSTI1-F GGGGAGGCGTCGAGTTCGGAGTTTATTA Chr13: 42464282- 117 EPSTI1-R AAAACTCGCTAAACGTCCCAACCGCATC 42464345 ADCY4 ADCY4-F CGGGTATTGTTGGTTTAGGTTGTAGTAGGT Chr14: 23873644- 123 ADCY4-R CGACCCTAACCAACCCCGAAACTCGAAA 23873710 HAPLN3 HAPLN3-F AGGGTAGAAAGGAAGCGGTAGTAGAAAA Chr15: 87239811- 116 HAPLN3-R ACAACAACTCCTCCCTTCGAACCCAACC 87239872 HSF4 HSF4-F TGTGGGAGGGAAGGGAAATCGAGATTGG Chr16: 65762053- 113 HSF4-R ACGACAAAACGAAACCCACAATCCTACCC 65762164 NBR1/ NBR1/TMEM10 ATTCGGATTGGTTAGTTTTTGCGGAAGT Chr17: 38719260-  91 TMEM106 A NBR1/TMEM10 TTCGCCACGCAACAACCTAAAACGCTAC 38719296 HAAO HAAO-F GGTTGCGGCGTTTATTTAGCGGGAAGTC Chr2: 42873761- 114 HAAO-R CTCGCCGAACCCGCGACGAAATCTAC 42873822 RARB RARB-F TAGAGGAATTTAAAGTGTGGGTTGGGGG Chr3: 25444371- 125 RARB-R ACCAACTTCTCTCCCTTTACGCCTTTTT 25444441 ALDH1L1 ALDH1L1-F TGGGTTAAGTATTTGTTATGTGTTACGGA Chr3: 127382511- 121 ALDH1L1-R CGCTATCCACCCGAATACGCAACT 127382580 HIST1H3G HIST1H3G-F GCGCGGCGTTTTGTTATCGGTGGATT Chr6: 26379588-  60 HIST1H3G-R TCTAAAATAACCCGCACCAAACAAACTACA 26379647 ZSCAN12 ZSCAN12-F TTATAAAGGTCGGAAGCGGTTACGGGGG Chr6: 28475534-  93 ZSCAN12-R AACCCCTTTCGCTCCCTTCCTAAAACGA 28475572 HCG4P6 HCG4P6-F GTATGGTTGCGATTTGGGGTTGGAAGGG Chr6: 30002983- 114 HCG4P6-R GCCGCGATCCAAAAACGCAAATCCTAAT 30003042 HLA-J-3 HLA-J,  TAGGGAATGTTTGGTTGCGATTTGGGG Chr6: 30083115-  80 NCRNA00  30083168 HLA-J, TCCTTACCGTCGTAAACATACTACTCAT NCRNA00 EYA4 EYA4-F GCGTAAGTGCGAGGTTGTCGGTAGC Chr6: 133604154- 125 EYA4-R TTTCCCGCAACTCTTTCCCCCTCTCT 133604229 HOXA7 HOXA7-F TGCGGTTAAAGAATTCGTTCGCGTTCGG Chr7: 27162955-  82 HOXA7-R CTAAACGCTCCCGCGAAACCTCCAAATC 27162982 USP44 USP44/p-F TTCGGGTATTTTGAGGTTGTCGTCGGGA Chr12: 94466379- 103 USP44/p-R GACGACGACGCGTCCGACGAATTTTA 94466481 CYP27A1 CYP27A1/p-F GTTTTGGTCGGGGCGTCGTGGATATTTT Chr2: 219354932- 111 CYP27A1/p-R AAAAACCAACTAAACCCCTTCCCGCTCG 219355042 PRSS3 PRSS3/p-F GTGTGGAAAGGGTTTGGCGGTTGTTAGG Chr9: 33740574- 113 PRSS3/p-R CTCGCCAAATACGTCCACCCAAAAACGA 33740686 C18orf62 C18orf62/p-F TAGGAGGGGACGTAGAGTTTACGGCGAA Chr18: 71296729- 105 C18orf62/p-R GAATACCCGACCCGACCCATCCATCAC 71296833 SFRP2 SFRP2/p-1-F TGCGTTTGTAGGAGAAGTCGGGTTGGTT Chr4: 154929326-  83 SFRP2/p-1-R ACTCTTCCTCGCCTCGCACTACTACCTA 154929408 SFRP2/p-2-F GTGCGATTCGGGGTTTCGAAAAGTTGGT Chr4: 154929535- 107 SFRP2/p-2-R GAAACTACGCGCGAACTTACAACGCCTC 154929641 SLCO4C1 SLCO4C1/p-F GAGCGTAGAGCGTTGAGCGGGG Chr5: 101660047- 123 SLCO4C1/p-R CGCCGCCGAATAACACGCCCAC 101660169 CORO1C CORO1C/p-1-F AGCGGGGATTTTCGGAGTTGGAGAGTTT Chr12: 107686622- 112 CORO1C/p-1-R CTCCATCCGCCCGACCTAACCCTAAAAA 107686733 CORO1C/p-2-F GGGAAGTGGCGTAGTGGGCGTTTGTATC Chr12: 107686752-  97 CORO1C/p-2-R TACCTCCAACGACCACGCCCACAAAATA 107686848 KJ904227 KJ904227/p-F TGGAGCGTTGAGTCGAAGTTTTGATTTT Chr3: 127489474- 109 KJ904227/p-R TCTTACCCGAACTTTAACCCCAACCGCT 127489582 C6orf141 C6orf141/ GGTTGGGAGTTCGGAGTTGTAGTAGAGG Chr6: 49626357-  99 p-1-F 49626455 C6orf141/ CTTTAACCGATTCAAACAACAAACGCCT p-1-R C6orf141/ GTAGGGCGCGGGGTTTCGTTAGTTTC Chr6: 49626570-  99 p-2-F 49626668 C6orf141/ ATCTACCGTTCTATCCTCGTAACCGCCG p-2-R BC030768 BC030768/p-F TCGTTTGGGAGGGATCGTTTTTGGGAGA Chr1: 26424688-  80 BC030768/p-R AACCCGAATACTATCCAACTACCGCCGC 26424767 DMRTA2 DMRTA2/p-F CGAGCGTGGGTATTAAGTCGGTAGTGGA Chr1: 50657067- 103 DMRTA2/p-R GACCTCAACCCCCTACGCCTAACCTACT 50657169 HFE HFE/p-1-F GTAGATCGCGGTTTTGTAGGGGCGTTTG Chr6: 26195692-  92 HFE/p-1-R CTAATTTCGATTTTTCCACCCCCGCCGC 26195783 HFE/p-2-F GAGTGTTTGTCGAGAAGGTTGAGTAAAT Chr6: 26196140-  82 HFE/p-2-R CACCGCCCAACGCATTCGTTCTAAAATA 26196221 CADPS2 CADPS2/p-F ATAAAAGTGGGGTGGGTGGCGGAGGG Chr7: 121744063- 104 CADPS2/p-R GCGCCGAAATAACAACCCAACCTACCAA 121744166 CYTH4 CYTH4/p-F TTTATCGGGGAAGTTTTCGAGGGTGGGC Chr22: 36050993- 120 CYTH4/p-R TCCCAACTACCTCCTACGCACGAACGAT 36051112 Intergenic Chr4/p-1-F ATGAAATGTGGTTCGTGGAAGGTGTTTGT Chr4: 186174475-  75 (Chr4) Chr4/p-1-R ACGACCCGAACGTTAATCCTCTTACTAC 186174549 NHLH2 NHLH2/p-F ACGTAGTTTTCGAGTTAGTGTCGTTAGAA Chr1: 116172677- 117 NHLH2/p-R GACAAACGCCTCAAACCCGACCG 116172793 NRN1 NRN1/p-F AGGAGCGGGAGAGGGAAAAATAGTTAAG Chr6: 5952635- 133 NRN1/p-R CGCTCCAAACTACGCCCAAAACTCAA 5952767 HMGCLL1 HMGCLL1/p-F ATTAGAGTTGTTTTGCGTATTGCGGCGG Chr6: 55551934-  97 HMGCLL1/p-R CAAATACCCCGTACACCCGCTACCCCAA 55552030 Me3 Me3/p-1-F GGGAGTTGAGGTTTACGCGGTTTCGTTG Chr11: 86061026-  99 Me3/p-1-R GACCGCCAACGCGATCCACCCATTAAC 86061124 Me3/p-2-F AGTTTTGGAAGTAGATTCGGTGCGGGTG Chr11: 86060867-  82 Me3/p-2-R GCCGCGCAATCGCCTCTTTTTCAC 86060948 Intergenic Chr3/p-1-F AGACGATAGATGGCGGGTAGGAAGGGAG Chr3: 135608250- 125 (Chr3) Chr3/p-1-R GCCGCCTACAACCGACGAACTACAAATC 135608374 Intergenic Chr8/p-1-F TCGCGGGTGAGGTTTGTGGTTAATTTCG Chr8: 68037553- 124 (Chr8) Chr8/p-1-R GCTCAACCAAACTACAACGTTCCCGCCT 68037676 NBPF1 NBPF1/p-F TGAGAGGCGTATTTTGTTGGTTACGGTT Chr1: 146219493-  82 NBPF1/p-R CGAAAACCATTCCGCTACCCTTCCAACT 146219574 Intergenic Chr10/p-1-F GGGGCGTTGGGTTATGGAGATTACGTTTT Chr10: 42748953- 101 (Chr10) Chr10/p-1-R GTCCCGCGCTTAACGAATTCTACGAACG 42749053 ASAP1 ASAP1/p-F GTTCGGGTAGGGGTCGGGGGTC Chr8: 131524437- 110 ASAP1/p-R CCCGAAACGACGTACTTAACGACCCGAA 131524546 Intergenic Chr1/p-1-F GGGAGGTTTGAGCGTCGAAGTTTTCGTT Chr1: 119352428- 122 (Chr1) Chr1/p-1-R GCCCACTACCCCGCGAAACCTTATCAAC 119352549 PPP2R5C PPP2R5C/p-F AGTCGTTAGGTTGTTAAGGCGCGTTGTG Chr14: 101317476-  59 PPP2R5C/p-R ACAAAAATAAAATCGAACCTAACCCCACG 101317534 Intergenic Chr2/p-1-F CGTATTAAGGGTTAAGCGGCGCGGT Chr22: 44883312-  93 (Chr2) Chr2/p-1-R AACTTTCTCGAACGACTCGATAAACCTAA 44883404 KRT78 KRT78/p-F AGGTTTTGGGAATTTGGAAGTTCGCGGG Chr12: 51554274-  97 KRT78/p-R AAAAACGCTCGAACCCAACCAATCGACG 51554370 LINC240 LINC240/ AAAGGAAGATCGTGGGTAGTTCGTGCG Chr6: 27167780-  80 p-1-F 27167859 LINC240/ ACTACAACTCACGTTTCCCCTCCAACAC p-1-R LINC240/ AGGTTTATTTGACGTTTTAGGTCGATAGT Chr6: 27172709- 122 p-2-F 27172830 LINC240/ CGATCTCTCCCTTTCTTCCGCTTCCTAA p-2-R Intergenic Chr16/p-1-F GGCGTCGGTTGCGGTTTTAGAT Chr16: 53648145- 125 (Chr16) Chr16/p-1-R ACGCGAAAATCTACCTTTTAATTACGAACC 53648269 HIST1H3G/ HIST1H3G/ TCGTCGGTGGTCGGCGCGTTTTT Chr6: 26379488- 102 1H2BI 1H2

26379589 HIST1H3G/ AACCCGCACCAAACAAACTACACGCAAA 1H2

PPM1H PPM1H/p-1-F GAATGGTAGCGAGAGGTTGCGGGTTAGG Chr12: 61312222-  89 PPM1H/p-1-R CTCTACCCTCAAAATCGCGACGCAAACG 61312310 PPM1H/p-2-F AGGAGTAGTATTGCGAGGGTGGAGGGTT Chr12: 61311917-  96 PPM1H/p-2-R CGCCAATCCCGCTCCGACACTATAACAA 61312012 TUBB2B TUBB2B/p-F ATAAGGTTTGGTGGAAGCGTAGGAGCGT Chr12: 3177175-  88 TUBB2B/p-R ACGATATTCTAACCTCCGCCGCGAAACT 3177262 C2CD4A C2C5F GGTAGAGGGATAGGGAAGAGTTTGGCGT Chr15: 60146378- 150 C2C5R ATTCAAAACGCGCGCGACGAAATTCAAC 60146528 COL19A1 COL2F GCGGAGTGGGAGGGTTATATTGGGAGAG Chr6: 70633134- 106 COL2R CCGAACAAAACTACGACACCGCCGAAAA 70633240 DCDC2 DCD5F ACGACGGGTTGAGATAGGTGGTTGGATT Chr6: 24465938-  90 DCD5R CCCGACGCGAAACAACGAACTAAAACGA 24466027 DHRS3 DGR2F TTTTTGTACGTTTTCGGGGTCGGAGGAG Chr1: 12601840- 102 DHR2R AATCGCCGTCTAAACAAATCGCGAACTA 12601942 GALNT3 GAL1F CGGCGGTCGCGGTTTGTAGTTTAGAATTG Chr2: 166358281- 150 GAL1R ACGCGCTTCCACTCCGACTAACAAATTA 166358431 GAL3F GGCGTCGTTCGGGTTAAGTTTGGTTGT Chr2: 166359152-  78 GAL3R CACAACTTACGCGAAACAACAACCTCGC 166359230 HES5 HES1F TGGGTTGGTGTCGCGCGAATTTTTGTTT Chr1: 2451234- 116 HES1R CCTCCTCCCGCAACTACGAAAACCGATA 2451350 HES3F GTTGGGGGTTATGTTTGGCGCGGAATAG Chr1: 2451478- 144 HES3R CGCCTATATAAAACGTCGACGCGCGAAA 2451622 HES4F GTTCGGGCGTCGCGGTCGTTTTTATATT Chr1: 2453144- 122 HES4R AAAACGCCCATTATACCCGCGCCAATTC 2453266 KILLIN KIL5F TAAGAATCGGCGGTAGTTAGTAGGCGGG Chr10: 89611638- 145 KIL5R TCCTACGCCGCGACGAAAACAAAAACTC 89611783 KIL6F AGGTGGGGCGCGTTTATTAGTTTAGGGG Chr10: 89611428- 150 KIL6R ACCTCTCCATCGCTAATACCCTACCGCT 89611578 MUC21 MUC2F GAGTGTTTCGAGGGTAGGAGGTTGTCGG Chr6: 31031426- 133 MUC2R CAAAAACCGCCCGCAAAACGAAACCTAA 31031559 NR2E1/ OST3F ACGGATCGATCGCGGTTTTGGTAAGGAT Chr6: 108542828-  87 OSTM1 OST3R CGCAAAAACGAAAAACTACGTACGCGCT 108542915 OST4F GTTGTTTGAGGACGGGTCGTTTAGCGG Chr6: 108543090-  99 OST4R ACCCCTATCCTACAACCCTACGAACGCA 108543189 PAMR1 PAM4F TTTCGGGAGGTGTGGTTACGTTTGGAGA Chr11: 35503958- 119 PAM4R CCCCTCCTCCCAACACCCAACACTAAAA 35504077 SCRN1 SCR2F GGTTGTGGTTTTTAAAAGGGAAAATTCGGG Chr7: 29996282- 106 SCR2R TAAACGCCGAAACCCGAACGTAACAACC 29996388 SEZ6 SEZ3F AGGTGATTAGAAGGGAGAGGGGGAGGTT Chr17: 24371083-  97 SEZ3R TCATTATACACGACGCGCCCCTCCAAAT 24371180 SEZ5F TACGTGGGTGTAGGTTAGGTCGGGTTGA Chr17: 24371224- 121 SEZ5R ACCACGCGACTACCGTATAAACAACCGAA 24371345

indicates data missing or illegible when filed

Equivalents

The above-described embodiments are intended to be examples only. Alterations, modifications and variations can be effected to the particular embodiments by those of skill in the art. The scope of the claims should not be limited by the particular embodiments set forth herein, but should be construed in a manner consistent with the specification as a whole.

REFERENCES

1. How K A, Nielsen H M, Tost J. DNA methylation based biomarkers: practical considerations and applications. Biochimie 2012; 94: 2314-37.

2. Mikeska T, Craig J M. DNA methylation biomarkers: cancer and beyond. Genes (Basel) 2014; 5: 821-64.

3. Noehammer C, Pulverer W, Hassler M R, Hofner M, Wielscher M, Vierlinger K, et al. Strategies for validation and testing of DNA methylation biomarkers. Epigenomics. 2014; 6: 603-22.

4. Warton K, Samimi G. Methylation of cell-free circulating DNA in the diagnosis of cancer. Front Mol.Biosci. 2015; 2: 13.

5. Wittenberger T, Sleigh S, Reisel D, Zikan M, Wahl B, Alunni-Fabbroni M, et al. DNA methylation markers for early detection of women's cancer: promise and challenges. Epigenomics. 2014; 6: 311-27.

6. Usadel H, Brabender J, Danenberg K D, Jeronimo C, Harden S, Engles J, et al. Quantitative adenomatous polyposis coli promoter methylation analysis in tumour tissue, serum, and plasma DNA of patients with lung cancer. Cancer Res. 2002; 62: 371-5.

7. Esteller M, Sanchez-Cespedes M, Rosell R, Sidransky D, Baylin S B, Herman J G. Detection of aberrant promoter hypermethylation of tumour suppressor genes in serum DNA from non-small cell lung cancer patients. Cancer Res. 1999; 59: 67-70.

8. Mazurek A, Pierzyna M, Giglok M, Dworzecka U, Suwinski R, Ma U E. Quantification of concentration and assessment of EGFR mutation in circulating DNA. Cancer Biomark. 2015; 15: 515-24.

9. Ostrow K L, Hoque M O, Loyo M, Brait M, Greenberg A, Siegfried J M, et al. Molecular analysis of plasma DNA for the early detection of lung cancer by quantitative methylation-specific PCR. Clin.Cancer Res. 2010; 16: 3463-72.

10. Powrozek T, Krawczyk P, Kucharczyk T, Milanowski J. Septin 9 promoter region methylation in free circulating DNA-potential role in noninvasive diagnosis of lung cancer: preliminary report. Med. Oncol. 2014; 31: 917.

11. Lin P C, Lin J K, Lin C H, Lin H H, Yang S H, Jiang J K, et al. Clinical Relevance of Plasma DNA Methylation in Colorectal Cancer Patients Identified by Using a Genome-Wide High-Resolution Array. Ann. Surg. Oncol. 2014.

12. Philipp A B, Nagel D, Stieber P, Lamerz R, Thalhammer I, Herbst A, et al. Circulating cell-free methylated DNA and lactate dehydrogenase release in colorectal cancer. BMC. Cancer 2014; 14: 245.

13. Chimonidou M, Strati A, Malamos N, Georgoulias V, Lianidou E S. SOX17 promoter methylation in circulating tumour cells and matched cell-free DNA isolated from plasma of patients with breast cancer. Clin. Chem. 2013; 59: 270-9.

14. Chimonidou M, Tzitzira A, Strati A, Sotiropoulou G, Sfikas C, Malamos N, et al. CST6 promoter methylation in circulating cell-free DNA of breast cancer patients. Clin. Biochem. 2013; 46: 235-40.

15. Martinez-Galan J, Torres-Torres B, Nunez M I, Lopez-Penalver J, Del M R, Ruiz De Almodovar J M, et al. ESR1 gene promoter region methylation in free circulating DNA and its correlation with estrogen receptor protein expression in tumour tissue in breast cancer patients. BMC. Cancer 2014; 14: 59.

16. Matuschek C, Bolke E, Lammering G, Gerber P A, Peiper M, Budach W, et al. Methylated APC and GSTP1 genes in serum DNA correlate with the presence of circulating blood tumour cells and are associated with a more aggressive and advanced breast cancer disease. Eur. J. Med. Res. 2010; 15: 277-86.

17. Fackler M J, Lopez B Z, Umbricht C, Teo W W, Cho S, Zhang Z, et al. Novel methylated biomarkers and a robust assay to detect circulating tumour DNA in metastatic breast cancer. Cancer Res. 2014; 74: 2160-70.

18. Avraham A, Uhlmann R, Shperber A, Birnbaum M, Sandbank J, Sella A, et al. Serum DNA methylation for monitoring response to neoadjuvant chemotherapy in breast cancer patients. Int. J. Cancer 2012; 131: E1166-E1172.

19. Sharma G, Mirza S, Parshad R, Gupta S D, Ralhan R. DNA methylation of circulating DNA: a marker for monitoring efficacy of neoadjuvant chemotherapy in breast cancer patients. Tumour. Biol. 2012; 33: 1837-43.

20. Legendre C, Gooden G C, Johnson K, Martinez R A, Liang W S, Saihia B. Whole-genome bisulfite sequencing of cell-free DNA identifies signature associated with metastatic breast cancer. Clin. Epigenetics. 2015; 7: 100.

21. Jones P A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 2012; 13: 484-92.

22. Cope L M, Fackler M J, Lopez-Bujanda Z, Wolff A C, Visvanathan K, Gray J W, et al. Do breast cancer cell lines provide a relevant model of the patient tumour methylome? PLoS. One. 2014; 9: e105545.

23. Becker D, Lutsik P, Ebert P, Bock C, Lengauer T, Walter J. BiQ Analyzer HiMod: an interactive software tool for high-throughput locus-specific analysis of 5-methylcytosine and its oxidized derivatives. Nucleic Acids Res. 2014; 42: W501-W507

24. Lutsik P, Feuerbach L, Arand J, Lengauer T, Walter J, Bock C. BiQ Analyzer HT: locus-specific analysis of DNA methylation by high-throughput bisulfite sequencing. Nucleic Acids Res. 2011; 39: W551-W556.

25. Soreide K. Receiver-operating characteristic curve analysis in diagnostic, prognostic and predictive biomarker research. J. Clin. Pathol. 2009; 62: 1-5.

26. Madic, J. et al. Pyrophosphorolysis-activated polymerization detects circulating tumor DNA in metastatic uveal melanoma. Clinical Cancer Research: an official journal of the American Association for Cancer Research. 2012; 18: 3934-3941.

27. Bidard, F. C. et al. Detection rate and prognostic value of circulating tumor cells and circulating tumor DNA in metastatic uveal melanoma. International Journal of Cancer 2014; 134: 1207-1213.

28. The Molecular Taxonomy of Primary Prostate Cancer. Cell. 2015; 163(4): 1011-25.

All references referred to herein are expressly incorporated by reference in their entireties. 

1-85. (canceled)
 86. A method of detecting a tumour, comprising: extracting DNA from a cell-free sample obtained from a subject, detecting multiple regions methylated in cancer from the DNA, wherein the multiple regions are within and across genes, wherein the multiple regions are methylated in a selected fraction of the tumours or tumour subtypes and are not methylated in normal tissues and normal blood cells; using probes that recognize both methylated and unmethylated DNA or are biased towards or specific to methylated DNA due to the presence of methylated CpG residues within the primers; and detecting at least one signal comprising at least two adjacent methylated sites within at least one of region the multiple regions; wherein the detection of at least one signal is indicative of a tumour.
 87. The method of claim 86, wherein the multiple regions that are methylated in a selected fraction of the tumours or tumour subtypes and are not methylated in normal tissues and normal blood cells are determined by: obtaining data for a population of samples comprising a plurality of genomic methylation data sets, each of said genomic methylation data sets associated with biological information for a corresponding sample; segregating the methylation data sets into a first group corresponding to one tissue or cell type exhibiting a tumour characteristic, a second group corresponding to a plurality of tissue or cell types not possessing the tumour characteristic, and a third group corresponding to a plurality of normal tissue or cell types; matching methylation data from the first group to methylation data from the second group, or methylation data from the third group, or methylation data from the second and third groups on a site-by-site basis across the genome; identifying a set of CpG sites that meet a predetermined threshold for establishing differential methylation between the first group and second group, or between the first group and third group, or between the first group and the second and third groups; identifying, using the set of CpG sites, target genomic regions comprising at least two differentially methylated CpGs with 300bp that meet said predetermined criteria; and extending the target genomic regions to encompass at least one adjacent differentially methylated CpG site that does not meet the predetermined criteria; wherein the extended target genomic regions provide a methylation signature indicative of the tumour characteristic.
 88. The method of claim 87, wherein: identifying further comprises limiting the set of CpG sites to CpG sites that further exhibit differential methylation with peripheral blood cells from control samples; or the predetermined threshold is indicative of methylation in the first group and non-methylation in the second group; or the predetermined threshold is met in at least 50% methylation of the samples in the first group; or the predetermined threshold is a difference in average methylation between the first group and second group of 0.3 or greater.
 89. The method of claim 87, wherein the tumour characteristic comprises one or more of: malignancy; a cancer type; a cancer classification; a molecular subtype classification; a cancer grade; a histological classification; a metabolic profile; a disease-associated mutation; a clinical outcome; and a drug response.
 90. The method of claim 86, further comprising determining sites of hydroxymethylation.
 91. The method of claim 90, wherein detecting comprises amplifying with primers designed to anneal to sequences having at least one methylated site therein.
 92. The method of claim 91, comprising one or more of: the primers are designed without preference as to location of the at least one methylated site within target sequences; the primers are designed to amplify DNA fragments 75 to 150 bp in length; the primers are designed to amplify DNA fragments comprising 3 to 12 CpG methylation sites; each of the regions is amplified in sections using multiple primer pairs; and the tumour signals comprise two or more adjacent methylation sites within the single sequencing read.
 93. The method of claim 91, wherein: amplifying is carried out with at least one primer set designed to amplify at least one methylation site having a methylation value at or below −0.1, −0.2, −0.3, −0.4, or −0.5 in normal issue; or amplifying is carried out with at least one primer set designed to amplify at least one methylation site having a difference between the average methylation value in the cancer and the normal tissue of greater than 0.1, 0.2, 0.3, 0.4, or 0.5; or amplifying is carried out with at least one primer set comprising primer pairs amplifying at least one methylation site having at least one adjacent methylation site within 200 base pairs that also has: a methylation value at or below −0.1, −0.2, −0.3, −0.4, or −0.5 in normal issue, and a difference between the average methylation value in the cancer and the normal tissue of greater than 0.1, 0.2, 0.3, 0.4, or 0.5.
 94. The method of claim 86, wherein the detection of at least one signal is indicative of a tumour during one or more of: determining response to treatment; monitoring tumour load; detecting residual tumour post-surgery; detecting relapse; use as a secondary screen; use as a primary screen; monitoring cancer development; and monitoring cancer risk.
 95. The method of claim 86, further comprising determining a distribution of tumour signals across the multiple regions; and: comparing the distribution to at least one pattern associated with a cancer; wherein similarity between the distribution and the pattern is indicative of the cancer; or comparing the distribution to a plurality of patterns, each one associated with a cancer type; wherein similarity between the distribution and one of the plurality of patterns is indicative of the associated cancer type.
 96. The method of claim 86, wherein the tumour is a breast cancer tumor, a prostate cancer tumour or a subtype thereof, a colon cancer tumour or a subtype thereof, a lung cancer tumour or a subtype thereof, or a uveal melanoma cancer tumour or a subtype thereof.
 97. The method of claim 86, wherein the regions comprise C2CD4A, COL 9A1, DCDC2, DHRS3, GALNT3, HES5, KILLIN, MUC21, NR2E1/OSTM1, PAMR1, SCRN1, and SEZ6, and wherein the tumour is uveal melanoma.
 98. The method of claim 97, comprising using probes including C2C5F, COL2F, DCD5F, DGR2F, GAL1F, GAL3F, HES1F, HES3F, HES4F, KIL5F, KIL6F, MUC2F, OST3F, OST4F, PAM4F, SCR2F, SEZ3F, and SEZ5F.
 99. The method of claim 86, wherein the regions comprise ADCY4, ALDH1L1, ALOX5, AMOTL2, ANXA2, CHST11, EFS, EPSTI1, EYA4, HAAO, HAPLN3, HCG4P6, HES5, HIF3A, HLA-F, HLA-J, HOXA7, HSF4, KLK4, LOC376693, LRRC4, NBR1, PAH, PON3, PPM1H, PTRF, RARA, RARB, RHCG, RND2,TMP4, TXNRD1, and ZSCAN12, and wherein the tumour is prostate cancer.
 100. The method of claim 99, comprising using probes including ADCY4-F, ALDH1L1-F, ALOX5-F, AMOTL2-F, ANXA2-F, CHST11-F, EFS-F, EPSTI1-F, EYA4-F, HAAO-F, HAPLN3-F, HCG4P6-F, HES5-F, HIF3A-F, HLA-F-F, HLA-J-1-F, HLA-J-2-F, HOXA7-F, HSF4-F, KLK4-F, LOC376693-F, LRRC4-F, NBR1-F, PAH-F, PON3-F, PPM1H-F, PTRF-F, RARA-F, RARB-F, RHCG-F, RND2-F, TMP4-F, TXNRD1-F, ZSCAN12-F, C1Dtrim, C1Etrim, CHSAtrim, DMBCtrim, FOXAtrim, FOXEtrim, SFRAtrim, SFRCtrim, SFREtrim, TTBAtrim, VWCJtrim, and VWCKtrim.
 101. The method of claim 86, wherein the regions comprise ASAP1, BC030768, C18orf62, C6orf141, CADPS2, CORO1C, CYP27A1, CYTH4, DMRTA2, EMX1, HFE, HIST1H3G/1H2BI, HMGCLL1, KCNK4, KJ904227, KRT78, LINC240, Me3, MIR1292, NBPF1, NHLH2, NRN1, PPM1H, PPP2R5C, PRSS3, SFRP2, SLCO4C1, SOX2OT, TUBB2B, USP44, Intergenic (Chr1), Intergenic (Chr2), Intergenic (Chr3), Intergenic (Chr4), Intergenic (Chr8), and Intergenic (Chr10), and wherein the tumour is aggressive prostate cancer.
 102. The method of claim 101, comprising using probes including ASAP1/p, BC030768/p, C18orf62/p, C6orf141/p-1, C6orf141/p-2, CADPS2/p, CORO1C/p-1, CORO1C/p-2, CYP27A1/p, CYTH4/p, DMRTA2/p, EMX1/p, HFE/p-1, HFE/p-2, HIST1H3G/1H2BI/p, HMGCLL1/p, KCNK4/p, KJ904227/p, KRT78/p, LINC240/p-1, LINC240/p-2, Me3/p-1, Me3/p-2, MIR129, NBPF1/p, NHLH2/p, NRN1/p, PPM1H/p-1, PPM1H/p-2, PPP2R5C/p, PRSS3/p, SFRP2/p-1, SFRP2/p-2, SLCO4C1/p, SOX2OT/p, TUBB2B/p, USP44/p, Chr1/p-1, Chr2/p-1, Chr3/p-1, Chr4/p-1, Chr8/p-1, and Chr10/p-1.
 103. The method of claim 86, wherein the regions comprise ALX1, ACVRL1, BRCA1, C1orf114, CA9, CARD11, CCL28, CD38, CDKL2, CHST11, CRYM, DMBX1, DPP10, DRD4, ERNA4, EPSTI1, EVX1, FABP5, FOXA3, GALR3, GIPC2, HINF1B, HOXA9, HOXB13, Intergenic5, Intergenic 8, IRF8, ITPRIPL1, LEF1, LOC641518, MAST1, BARHL2, BOLL, C5orf39, DDAH2, DMRTA2, GABRA4, ID4, IRF4, NT5E, SIM1, TBX15, NFIC, NPHS2, NR5A2, OTX2, PAX6, GNG4, SCAND3, TAL1, PDX1, PHOX2B, POU4F1, PFIA3, PRDM13, PRKCB, PRSS27, PTGDR, PTPRN2, SALL3, SLC7A4, SOX2OT, SPAG6, TCTEX1D1, TMEM132C, TMEM90B, TNFRSF10D, TOP2P1, TSPAN33, TTBK1, UDB, and VWC2, and wherein the tumour is triple negative breast cancer (TNBC).
 104. The method of claim 103, comprising using probes including ALX1, AVCRL1, BRCA1-A, C1Dtrim, C1Etrim, CA9-A, CARD11-B, CCL28-A, CD38, CDKL2-A, CHSAtrim, CRYM-A, DMBCtrim, DMRTA2exp-A, DPP10-A, DPP10-B, DPP10-C, DRD4-A, EFNA4-B, EPSTI1, EVX1, FABP5, FOXAtrim, FOXEtrim, GALR3-A, GIPC2-A, HINF C trim, HOXAAtrim, HOXACtrim, HOXB13-A, Int5, Int8, IRF8-A, ITRIPL1, LEF1-A, MAST1 A trim, mbBARHL2 Trim, mbBOLL Trim, mbC5orf Trim, mbDDAH Trim, mbDMRTA Trim, mbGABRA A Trim, mbGABRA B Trim, mbGNG Trim, mbID4 Trim, mbIRF Trim, mbNT5E Trim, mbSIM A Trim, mbTBX15 Trim, NFIC-B, NFIC-A, NPSH2-B, NR5A2-B, OTX2-A, PAX6-A, pbDMRTA Trim, pbGNG Trim, pbSCAND Trim, pbTAL Trim, PDX1exp-B, PHOX2B-A, POU4F1 A trim, PPFIA3-A, PRDM13, PRKCB-A, PRKCB-C, PRSS27-A, PTGDR, PTPRN2-A, PTPRN2-B, SALL3-A, SALL3-B, SLC7A4-A, SOX2OT-B, SPAG6 A trim, TCTEX1D1-A, TMEM-A, TMEM-B, TMEM90B-A, TNFRSF10D, TOP2P1-B, TSPAN33-A, TTBAtrim, UBD-A, VWCJtrim, and VWCKtrim.
 105. A kit for detecting a tumour comprising reagents for carrying out the method of claim 1, and instructions for detecting tumour signals. 